Patent Grant
11589823
The present invention relates generally to systems and methods for processing data received from a glucose sensor. Particularly, the present invention relates to systems and methods for detecting and replacing transient non-glucose related signal artifacts, including detecting, estimating, predicting and otherwise minimizing the effects of signal artifacts in a glucose sensor data stream.
Diabetes mellitus is a disorder in which the pancreas cannot create sufficient insulin (Type I or insulin dependent) and/or in which insulin is not effective (Type 2 or non-insulin dependent). In the diabetic state, the victim suffers from high blood sugar, which causes an array of physiological derangements (kidney failure, skin ulcers, or bleeding into the vitreous of the eye) associated with the deterioration of small blood vessels. A hypoglycemic reaction (low blood sugar) is induced by an inadvertent overdose of insulin, or after a normal dose of insulin or glucose-lowering agent accompanied by extraordinary exercise or insufficient food intake.
Conventionally, a diabetic person carries a self-monitoring blood glucose (SMBG) monitor, which typically comprises uncomfortable finger pricking methods. Due to the lack of comfort and convenience, a diabetic will normally only measure his or her glucose level two to four times per day. Unfortunately, these time intervals are so far spread apart that the diabetic will likely find out too late, sometimes incurring dangerous side effects, of a hyperglycemic or hypoglycemic condition. In fact, it is not only unlikely that a diabetic will take a timely SMBG value, but additionally the diabetic will not know if their blood glucose value is going up (higher) or down (lower) based on conventional methods.
Consequently, a variety of transdermal and implantable electrochemical sensors are being developed for continuous detecting and/or quantifying blood glucose values. Many implantable glucose sensors suffer from complications within the body and provide only short-term and less-than-accurate sensing of blood glucose. Similarly, transdermal sensors have run into problems in accurately sensing and reporting back glucose values continuously over extended periods of time. Some efforts have been made to obtain blood glucose data from implantable devices and retrospectively determine blood glucose trends for analysis, however these efforts do not aid the diabetic in determining real-time blood glucose information. Some efforts have also been made to obtain blood glucose data from transdermal devices for prospective data analysis, however similar problems have occurred.
Data streams from glucose sensors are known to have some amount of noise, caused by unwanted electronic and/or diffusion-related system noise that degrades the quality of the data stream. Some attempts have been made in conventional glucose sensors to smooth the raw output data stream representative of the concentration of blood glucose in the sample, for example by smoothing or filtering of Gaussian, white, random, and/or other relatively low amplitude noise in order to improve the signal to noise ratio, and thus data output.
Systems and methods are provided that accurately detect and replace signal noise that is caused by substantially non-glucose reaction rate-limiting phenomena, such as ischemia, pH changes, temperature changes, pressure, and stress, for example, which are referred to herein as signal artifacts. Detecting and replacing signal artifacts in a raw glucose data can provide accurate estimated glucose measurements to a diabetic patient so that they can proactively care for their condition to safely avoid hyperglycemic and hypoglycemic conditions.
In a first embodiment a method is provided for analyzing data from a glucose sensor, including: monitoring a data stream from the sensor; detecting transient non-glucose related signal artifacts in the data stream that have a higher amplitude than a system noise; and replacing at least some of the signal artifacts using estimated glucose signal values.
In an aspect of the first embodiment, the data signal obtaining step includes receiving data from one of non-invasive, minimally invasive, and invasive glucose sensor.
In an aspect of the first embodiment, the data signal obtaining step includes receiving data from one of an enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, and radiometric glucose sensor.
In an aspect of the first embodiment, the data signal obtaining step includes receiving data from a wholly implantable glucose sensor.
In an aspect of the first embodiment, the signal artifacts detection step includes testing for ischemia within or proximal to the glucose sensor.
In an aspect of the first embodiment, the ischemia testing step includes obtaining oxygen concentration using an oxygen sensor proximal to or within the glucose sensor.
In an aspect of the first embodiment, the ischemia testing step includes comparing a measurement from an oxygen sensor proximal to or within the glucose sensor with a measurement from an oxygen sensor distal from the glucose sensor.
In an aspect of the first embodiment, the glucose sensor includes an electrochemical cell including a working electrode and a reference electrode, and wherein the ischemia-testing step includes pulsed amperometric detection.
In an aspect of the first embodiment, the glucose sensor includes an electrochemical cell including working, counter and reference electrodes, and wherein the ischemia-testing step includes monitoring the counter electrode.
In an aspect of the first embodiment, the glucose sensor includes an electrochemical cell including working, counter and reference electrodes, and wherein the ischemia-testing step includes monitoring the reference electrode.
In an aspect of the first embodiment, the glucose sensor includes an electrochemical cell including an anode and a cathode, and wherein the ischemia-testing step includes monitoring the cathode.
In an aspect of the first embodiment, the signal artifacts detection step includes monitoring a level of pH proximal to the sensor.
In an aspect of the first embodiment, the signal artifacts detection step includes monitoring a temperature proximal to the sensor.
In an aspect of the first embodiment, the signal artifacts detection step includes comparing a level of pH proximal to and distal to the sensor.
In an aspect of the first embodiment, the signal artifacts detection step includes comparing a temperature proximal to and distal to the sensor.
In an aspect of the first embodiment, the signal artifacts detection step includes monitoring a pressure or stress within the glucose sensor.
In an aspect of the first embodiment, the signal artifacts detection step includes evaluating historical data for high amplitude noise above a predetermined threshold.
In an aspect of the first embodiment, the signal artifacts detection step includes a Cone of Possibility Detection Method.
In an aspect of the first embodiment, the signal artifacts detection step includes evaluating the data stream for a non-physiological rate-of-change.
In an aspect of the first embodiment, the signal artifacts detection step includes monitoring the frequency content of the signal.
In an aspect of the first embodiment, the frequency-content monitoring step includes performing an orthogonal basis function-based transform.
In an aspect of the first embodiment, the transform is a Fourier Transform or a wavelet transform.
In an aspect of the first embodiment, the artifacts replacement step includes performing linear or non-linear regression.
In an aspect of the first embodiment, the artifacts replacement step includes performing a trimmed mean.
In an aspect of the first embodiment, the artifacts replacement step includes filtering using a non-recursive filter.
In an aspect of the first embodiment, the non-recursive filtering step uses a finite impulse response filter.
In an aspect of the first embodiment, the artifacts replacement step includes filtering using a recursive filter.
In an aspect of the first embodiment, the recursive filtering step uses an infinite impulse response filter.
In an aspect of the first embodiment, the artifacts replacement step includes a performing a maximum average algorithm.
In an aspect of the first embodiment, the artifacts replacement step includes performing a Cone of Possibility Replacement Method.
In an aspect of the first embodiment, the method further includes estimating future glucose signal values based on historical glucose values.
In an aspect of the first embodiment, the glucose future estimation step includes algorithmically estimating the future signal value based using at least one of linear regression, non-linear regression, and an auto-regressive algorithm.
In an aspect of the first embodiment, the glucose future estimation step further includes measuring at least one of rate-of-change, acceleration, and physiologically feasibility of one or more signal values and subsequently selectively applying the algorithm conditional on a range of one of the measurements.
In an aspect of the first embodiment, the glucose sensor includes an electrochemical cell including working, counter, and reference electrodes, and wherein the artifacts replacement step includes normalizing the data signal based on baseline drift at the reference electrode.
In an aspect of the first embodiment, the signal artifacts replacement step is substantially continual.
In an aspect of the first embodiment, the signal artifacts replacement step is initiated in response to positive detection of signal artifacts.
In an aspect of the first embodiment, the signal artifacts replacement step is terminated in response to detection of negligible signal artifacts.
In an aspect of the first embodiment, the signal artifacts detection step includes evaluating the severity of the signal artifacts.
In an aspect of the first embodiment, the severity evaluation is based on an amplitude of the transient non-glucose related signal artifacts.
In an aspect of the first embodiment, the severity evaluation is based on a duration of the transient non-glucose related signal artifacts.
In an aspect of the first embodiment, the severity evaluation is based on a rate-of-change of the transient non-glucose related signal artifacts.
In an aspect of the first embodiment, the severity evaluation is based on a frequency content of the transient non-glucose related signal artifacts.
In an aspect of the first embodiment, the artifacts replacement step includes selectively applying one of a plurality of signal estimation algorithm factors in response to the severity of the signal artifacts.
In an aspect of the first embodiment, the plurality of signal estimation algorithm factors includes a single algorithm with a plurality of parameters that are selectively applied to the algorithm.
In an aspect of the first embodiment, the plurality of signal estimation algorithm factors includes a plurality of distinct algorithms.
In an aspect of the first embodiment, the step of selectively applying one of a plurality of signal estimation algorithm factors includes selectively applying a predetermined algorithm that includes a set of parameters whose values depend on the severity of the signal artifacts.
In an aspect of the first embodiment, the method further includes discarding at least some of the signal artifacts.
In an aspect of the first embodiment, the method further includes projecting glucose signal values for a time during which no data is available.
In a second embodiment, a method is provided for processing data signals obtained from a glucose sensor including: obtaining a data stream from a glucose sensor; detecting transient non-glucose related signal artifacts in the data stream that have a higher amplitude than a system noise; and selectively applying one of a plurality of signal estimation algorithm factors to replace non-glucose related signal artifacts.
In an aspect of the second embodiment, the data signal obtaining step includes receiving data from one of non-invasive, minimally invasive, and invasive glucose sensor.
In an aspect of the second embodiment, the data signal obtaining step includes receiving data from one of an enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, and radiometric glucose sensor.
In an aspect of the second embodiment, the data signal obtaining step includes receiving data from a wholly implantable glucose sensor.
In an aspect of the second embodiment, the signal artifacts detection step includes testing for ischemia within or proximal to the glucose sensor.
In an aspect of the second embodiment, the ischemia testing step includes obtaining oxygen concentration using an oxygen sensor proximal to or within the glucose sensor.
In an aspect of the second embodiment, the ischemia testing step includes comparing a measurement from an oxygen sensor proximal to or within the glucose sensor with a measurement from an oxygen sensor distal from the glucose sensor.
In an aspect of the second embodiment, the glucose sensor includes an electrochemical cell including a working electrode and a reference electrode, and wherein the ischemia-testing step includes pulsed amperometric detection.
In an aspect of the second embodiment, the glucose sensor includes an electrochemical cell including working, counter and reference electrodes, and wherein the ischemia-testing step includes monitoring the counter electrode.
In an aspect of the second embodiment, the glucose sensor includes an electrochemical cell including working, counter and reference electrodes, and wherein the ischemia testing step includes monitoring the reference electrode.
In an aspect of the second embodiment, the glucose sensor includes an electrochemical cell including an anode and a cathode, and wherein the ischemia-testing step includes monitoring the cathode.
In an aspect of the second embodiment, the signal artifacts detection step includes monitoring a level of pH proximal to the sensor.
In an aspect of the second embodiment, the signal artifacts detection step includes monitoring a temperature proximal to the sensor.
In an aspect of the second embodiment, the signal artifacts detection step includes comparing a level of pH proximal to and distal to the sensor.
In an aspect of the second embodiment, the signal artifacts detection step includes comparing a temperature proximal to and distal to the sensor.
In an aspect of the second embodiment, the signal artifacts detection step includes monitoring the pressure or stress within the glucose sensor.
In an aspect of the second embodiment, the signal artifacts detection step includes evaluating historical data for high amplitude noise above a predetermined threshold.
In an aspect of the second embodiment, the signal artifacts detection step includes a Cone of Possibility Detection Method.
In an aspect of the second embodiment, the signal artifacts detection step includes evaluating the signal for a non-physiological rate-of-change.
In an aspect of the second embodiment, the signal artifacts detection step includes monitoring the frequency content of the signal.
In an aspect of the second embodiment, the frequency-content monitoring step includes performing an orthogonal basis function-based transform.
In an aspect of the second embodiment, the transform is a Fourier Transform or a wavelet transform.
In an aspect of the second embodiment, the artifacts replacement step includes performing linear or non-linear regression.
In an aspect of the second embodiment, the artifacts replacement step includes performing a trimmed mean.
In an aspect of the second embodiment, the artifacts replacement step includes filtering using a non-recursive filter.
In an aspect of the second embodiment, the non-recursive filtering step uses a finite impulse response filter.
In an aspect of the second embodiment, the artifacts replacement step includes filtering using a recursive filter.
In an aspect of the second embodiment, the recursive filtering step uses an infinite impulse response filter.
In an aspect of the second embodiment, the artifacts replacement step includes a performing a maximum average algorithm.
In an aspect of the second embodiment, the artifacts replacement step includes performing a Cone of Possibility algorithm.
In an aspect of the second embodiment, the method further includes estimating future glucose signal values based on historical glucose values.
In an aspect of the second embodiment, the glucose future estimation step includes algorithmically estimating the future signal value based using at least one of linear regression, non-linear regression, and an auto-regressive algorithm.
In an aspect of the second embodiment, the glucose future estimation step further includes measuring at least one of rate-of-change, acceleration, and physiologically feasibility of one or more signal values and subsequently selectively applying the algorithm conditional on a range of one of the measurements.
In an aspect of the second embodiment, the glucose sensor includes an electrochemical cell including working, counter, and reference electrodes, and wherein the artifacts replacement step includes normalizing the data signal based on baseline drift at the reference electrode.
In an aspect of the second embodiment, the selective application step is substantially continual.
In an aspect of the second embodiment, the selective application step is initiated in response to positive detection of signal artifacts.
In an aspect of the second embodiment, the selective application step is terminated in response to detection of negligible signal artifacts.
In an aspect of the second embodiment, the signal artifacts detection step includes evaluating the severity of the signal artifacts.
In an aspect of the second embodiment, the severity evaluation is based on an amplitude of the transient non-glucose related signal artifacts.
In an aspect of the second embodiment, the severity evaluation is based on a duration of the transient non-glucose related signal artifacts.
In an aspect of the second embodiment, the severity evaluation is based on a rate-of-change of the transient non-glucose related signal artifacts.
In an aspect of the second embodiment, the severity evaluation is based on a frequency content of the transient non-glucose related signal artifacts.
In an aspect of the second embodiment, the selective application step applies the one of a plurality of signal estimation algorithm factors in response to the severity of the signal artifacts.
In an aspect of the second embodiment, the plurality of signal estimation algorithm factors includes a single algorithm with a plurality of parameters that are selectively applied to the algorithm.
In an aspect of the second embodiment, the plurality of signal estimation algorithm factors includes a plurality of distinct algorithms.
In an aspect of the second embodiment, the selective application step includes selectively applying a predetermined algorithm that includes a set of parameters whose values depend on the severity of the signal artifacts.
In an aspect of the second embodiment, the method further includes discarding at least some of the signal artifacts.
In an aspect of the second embodiment, the selective application step further includes projecting glucose signal values for a time during which no data is available.
In a third embodiment, a system is provided for processing data signals obtained from a glucose sensor, including: a signal processing module including programming to monitor a data stream from the sensor over a period of time; a detection module including programming to detect transient non-glucose related signal artifacts in the data stream that have a higher amplitude than a system noise; and a signal estimation module including programming to replace at least some of the signal artifacts with estimated glucose signal values.
In an aspect of the third embodiment, the signal processing module is adapted to receive data from one of non-invasive, minimally invasive, and invasive glucose sensor.
In an aspect of the third embodiment, the signal processing module is adapted to receive data from one of an enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, and radiometric glucose sensor.
In an aspect of the third embodiment, the signal processing module is adapted to receive data from a wholly implantable glucose sensor.
In an aspect of the third embodiment, the detection module includes programming to for ischemia detection.
In an aspect of the third embodiment, the detection module includes programming to detect ischemia from a first oxygen sensor located proximal to or within the glucose sensor.
In an aspect of the third embodiment, the detection module further includes programming to compare a measurement from a first oxygen sensor located proximal to or within the glucose sensor with a measurement from a second oxygen sensor located distal to the glucose sensor for ischemia detection.
In an aspect of the third embodiment, the detection module further includes programming to detect ischemia using pulsed amperometric detection of an electrochemical cell including a working electrode and a reference electrode.
In an aspect of the third embodiment, the detection module further includes programming to detect ischemia by monitoring a counter electrode of an electrochemical cell that includes working, counter and reference electrodes.
In an aspect of the third embodiment, the detection module further includes programming to detect ischemia by monitoring a reference electrode of an electrochemical cell that includes working, counter and reference electrodes.
In an aspect of the third embodiment, the detection module further includes programming to detect ischemia by monitoring a cathode of an electrochemical cell.
In an aspect of the third embodiment, the detection module monitors a level of pH proximal to the glucose sensor.
In an aspect of the third embodiment, the detection module monitors a temperature proximal to the glucose sensor.
In an aspect of the third embodiment, the detection module compares a level of pH proximal to and distal to the sensor.
In an aspect of the third embodiment, the detection module compares a temperature proximal to and distal to the glucose sensor.
In an aspect of the third embodiment, the detection module monitors a pressure or stress within the glucose sensor.
In an aspect of the third embodiment, the detection module evaluates historical data for high amplitude noise above a predetermined threshold.
In an aspect of the third embodiment, the detection module includes programming to perform a Cone of Possibility to detect signal artifacts.
In an aspect of the third embodiment, the detection module evaluates the data stream for a non-physiological rate-of-change.
In an aspect of the third embodiment, the detection module monitors the frequency content of the signal.
In an aspect of the third embodiment, the detection module monitors the frequency content including performing an orthogonal basis function-based transform.
In an aspect of the third embodiment, the orthogonal basis function-based transform includes a Fourier Transform or a wavelet transform.
In an aspect of the third embodiment, the signal estimation module estimates glucose signal values using linear or non-linear regression.
In an aspect of the third embodiment, the signal estimation module estimates glucose signal values using a trimmed mean.
In an aspect of the third embodiment, the signal estimation module estimates glucose signal values using a non-recursive filter.
In an aspect of the third embodiment, the non-recursive filter is a finite impulse response filter.
In an aspect of the third embodiment, the signal estimation module estimates glucose signal values using a recursive filter.
In an aspect of the third embodiment, the recursive filter is an infinite impulse response filter.
In an aspect of the third embodiment, the signal estimation module estimates glucose signal values using a maximum average algorithm.
In an aspect of the third embodiment, the signal estimation module estimates glucose signal values using a Cone of Possibility Replacement Method.
In an aspect of the third embodiment, the signal estimation module further includes programming to estimate future glucose signal values based on historical glucose values.
In an aspect of the third embodiment, the future glucose signal value programming includes algorithmically estimating the future signal value based using at least one of linear regression, non-linear regression, and an auto-regressive algorithm.
In an aspect of the third embodiment, the signal estimation module further includes programming to measure at least one of rate-of-change, acceleration, and physiologically feasibility of one or more signal values, and wherein the signal estimation module further includes programming to selectively apply an algorithm responsive to value of one of the measurements from the detection module.
In an aspect of the third embodiment, signal estimation module includes programming to normalize the data stream based on baseline drift at a reference electrode of a glucose sensor that includes an electrochemical cell including working, counter, and reference electrodes.
In an aspect of the third embodiment, the signal estimation module continually replaces the data stream with estimated signal values.
In an aspect of the third embodiment, the signal estimation module initiates signal replacement of the data stream in response to positive detection of signal artifacts.
In an aspect of the third embodiment, the signal estimation module terminates signal replacement in response to detection of negligible signal artifacts.
In an aspect of the third embodiment, the detection module evaluates the severity of the signal artifacts.
In an aspect of the third embodiment, the detection module evaluates the severity of the signal artifacts based on an amplitude of the transient non-glucose related signal artifacts.
In an aspect of the third embodiment, the detection module evaluates the severity of the signal artifacts based on a duration of the transient non-glucose related signal artifacts.
In an aspect of the third embodiment, the detection module evaluates the severity of the signal artifacts based on a rate-of-change of the transient non-glucose related signal artifacts.
In an aspect of the third embodiment, the detection module evaluates the severity of the signal artifacts based on a frequency content of the transient non-glucose related signal artifacts.
In an aspect of the third embodiment, the signal estimation module includes programming to selectively apply one of a plurality of signal estimation algorithm factors in response to the severity of the signal artifacts.
In an aspect of the third embodiment, the plurality of signal estimation algorithm factors includes a single algorithm with a plurality of parameters that are selectively applied to the algorithm.
In an aspect of the third embodiment, the plurality of signal estimation algorithm factors includes a plurality of distinct algorithms.
In an aspect of the third embodiment, the signal estimation module selectively applies a set of parameters whose values depend on the severity of the signal artifacts to one of a predetermined algorithm.
In an aspect of the third embodiment, the detection module includes programming to discard at least some of the signal artifacts.
In an aspect of the third embodiment, the signal estimation module includes programming to project glucose signal values for a time during which no data is available.
In a fourth embodiment, a system is provided for processing data signals obtained from a glucose sensor, the system including: a signal processing module including programming to monitor a data stream from the sensor over a period of time; a detection module including programming to detect transient non-glucose related signal artifacts in the wherein the plurality of signal estimation algorithm factors include a plurality of distinct algorithms data streams that have a higher amplitude than a system noise; and a signal estimation module including programming to selectively apply one of a plurality of signal estimation algorithm factors to replace non-glucose related signal artifacts.
In an aspect of the fourth embodiment, the signal processing module is adapted to receive data from one of non-invasive, minimally invasive, and invasive glucose sensor.
In an aspect of the fourth embodiment, the signal processing module is adapted to receive data from one of an enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, and radiometric glucose sensor.
In an aspect of the fourth embodiment, the signal processing module is adapted to receive data from a wholly implantable glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to detect ischemia within or proximal to the glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to obtain oxygen concentration using an oxygen sensor proximal to or within the glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to compare a measurement from an oxygen sensor proximal to or within the glucose sensor with a measurement from an oxygen sensor distal from the glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to detect ischemia using pulsed amperometric detection of an electrochemical cell that includes a working electrode and a reference electrode.
In an aspect of the fourth embodiment, the detection module includes programming to monitor a counter electrode of a glucose sensor that includes an electrochemical cell including working, counter and reference electrodes.
In an aspect of the fourth embodiment, the detection module includes programming to monitor a reference electrode of a glucose sensor that includes an electrochemical cell including working, counter and reference electrodes.
In an aspect of the fourth embodiment, the detection module includes programming to monitor a cathode of a glucose sensor that includes an electrochemical cell including an anode and a cathode.
In an aspect of the fourth embodiment, the detection module includes programming to monitor a level of pH proximal to the glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to monitor a temperature proximal to the glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to compare a level of pH proximal to and distal to the glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to compare a temperature proximal to and distal to the sensor.
In an aspect of the fourth embodiment, the detection module includes programming to monitor a pressure or stress within the glucose sensor.
In an aspect of the fourth embodiment, the detection module includes programming to evaluate historical data for high amplitude noise above a predetermined threshold.
In an aspect of the fourth embodiment, the detection module includes programming to perform Cone of Possibility Detection.
In an aspect of the fourth embodiment, the detection module includes programming to evaluate the signal for a non-physiological rate-of-change.
In an aspect of the fourth embodiment, the detection module includes programming to monitor the frequency content of the signal.
In an aspect of the fourth embodiment, the detection module performs an orthogonal basis function-based transform to monitor frequency content.
In an aspect of the fourth embodiment, the transform is a Fourier Transform or a wavelet transform.
In an aspect of the fourth embodiment, the signal estimation module estimates glucose signal values using linear or non-linear regression.
In an aspect of the fourth embodiment, the signal estimation module estimates glucose signal values using a trimmed mean.
In an aspect of the fourth embodiment, the signal estimation module estimates glucose signal values using a non-recursive filter.
In an aspect of the fourth embodiment, the non-recursive filter is a finite impulse response filter.
In an aspect of the fourth embodiment, the signal estimation module estimates glucose signal values using a recursive filter.
In an aspect of the fourth embodiment, the recursive filter is an infinite impulse response filter.
In an aspect of the fourth embodiment, the signal estimation module estimates glucose signal values using a maximum average algorithm.
In an aspect of the fourth embodiment, the signal estimation module estimates glucose signal values using Cone of Possibility Replacement Method algorithm.
In an aspect of the fourth embodiment, the signal estimation module further includes programming to estimate future glucose signal values based on historical glucose values.
In an aspect of the fourth embodiment, future glucose signal value programming includes algorithmically estimating the future signal value based using at least one of linear regression, non-linear regression, and an auto-regressive algorithm.
In an aspect of the fourth embodiment, the signal estimation module further includes programming to measure at least one of rate-of-change, acceleration, and physiologically feasibility of one or more signal values, and wherein the signal estimation module further includes programming to selectively apply an algorithm responsive to value of one of the measurements from the detection module.
In an aspect of the fourth embodiment, the signal estimation module includes programming to normalize the data stream based on baseline drift at a reference electrode of a glucose sensor that includes an electrochemical cell including working, counter, and reference electrodes.
In an aspect of the fourth embodiment, the signal estimation module continually replaces the data stream with estimated signal values.
In an aspect of the fourth embodiment, the signal estimation module initiates signal replacement of the data stream in response to positive detection of signal artifacts.
In an aspect of the fourth embodiment, the signal estimation module terminates signal replacement in response to detection of negligible signal artifacts.
In an aspect of the fourth embodiment, the detection module evaluates the severity of the signal artifacts.
In an aspect of the fourth embodiment, the detection module evaluates the severity of the signal artifacts based on an amplitude of the transient non-glucose related signal artifacts.
In an aspect of the fourth embodiment, the detection module evaluates the severity of the signal artifacts based on a duration of the transient non-glucose related signal artifacts.
In an aspect of the fourth embodiment, the detection module evaluates the severity of the signal artifacts based on a rate-of-change of the transient non-glucose related signal artifacts.
In an aspect of the fourth embodiment, the detection module evaluates the severity of the signal artifacts based on a frequency content of the transient non-glucose related signal artifacts.
In an aspect of the fourth embodiment, the signal estimation module includes programming to selectively apply one of a plurality of signal estimation algorithm factors in response to the severity of the signal artifacts.
In an aspect of the fourth embodiment, the plurality of signal estimation algorithm factors includes a single algorithm with a plurality of parameters that are selectively applied to the algorithm.
In an aspect of the fourth embodiment, the plurality of signal estimation algorithm factors includes a plurality of distinct algorithms.
In an aspect of the fourth embodiment, the signal estimation module selectively applies a set of parameters whose values depend on the severity of the signal artifacts to one of a predetermined algorithm.
In an aspect of the fourth embodiment, the detection module includes programming to discard at least some of the signal artifacts.
In an aspect of the fourth embodiment, the signal estimation module includes programming to project glucose signal values for a time during which no data is available.
In a fifth embodiment, an implantable glucose monitoring device is provided including: a glucose sensor; and a processor operatively linked to the sensor designed to receive a data stream from the sensor; wherein the processor is programmed to analyze the data stream and to detect transient non-glucose related signal artifacts in the data stream that have a higher amplitude than system noise, and to replace at least some of the signal artifacts with estimated values.
The following description and examples illustrate some exemplary embodiments of the disclosed invention in detail. Those of skill in the art will recognize that there are numerous variations and modifications of this invention that are encompassed by its scope. Accordingly, the description of a certain exemplary embodiment should not be deemed to limit the scope of the present invention.
In order to facilitate an understanding of the preferred embodiments, a number of terms are defined below.
The term “EEPROM,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, electrically erasable programmable read-only memory, which is user-modifiable read-only memory (ROM) that can be erased and reprogrammed (e.g., written to) repeatedly through the application of higher than normal electrical voltage.
The term “SRAM,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, static random access memory (RAM) that retains data bits in its memory as long as power is being supplied.
The term “A/D Converter,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, hardware and/or software that converts analog electrical signals into corresponding digital signals.
The term “microprocessor,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation a computer system or processor designed to perform arithmetic and logic operations using logic circuitry that responds to and processes the basic instructions that drive a computer.
The term “RF transceiver,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a radio frequency transmitter and/or receiver for transmitting and/or receiving signals.
The term “jitter,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, noise above and below the mean caused by ubiquitous noise caused by a circuit and/or environmental effects; jitter can be seen in amplitude, phase timing, or the width of the signal pulse.
The terms “raw data stream” and “data stream,” as used herein, are broad terms and are used in their ordinary sense, including, without limitation, an analog or digital signal directly related to the measured glucose from the glucose sensor. In one example, the raw data stream is digital data in “counts” converted by an A/D converter from an analog signal (e.g., voltage or amps) representative of a glucose concentration. The terms broadly encompass a plurality of time spaced data points from a substantially continuous glucose sensor, which comprises individual measurements taken at time intervals ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes or longer.
The term “counts,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a unit of measurement of a digital signal. In one example, a raw data stream measured in counts is directly related to a voltage (e.g., converted by an A/D converter), which is directly related to current from the working electrode. In another example, counter electrode voltage measured in counts is directly related to a voltage.
The terms “glucose sensor” and “member for determining the amount of glucose in a biological sample,” as used herein, are broad terms and are used in an ordinary sense, including, without limitation, any mechanism (e.g., enzymatic or non-enzymatic) by which glucose can be quantified. For example, some embodiments utilize a membrane that contains glucose oxidase that catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconate, as illustrated by the following chemical reaction:
Glucose+O2→Gluconate+H2O2
Because for each glucose molecule metabolized, there is a proportional change in the co-reactant O2 and the product H2O2, one can use an electrode to monitor the current change in either the co-reactant or the product to determine glucose concentration.
The terms “operably connected” and “operably linked,” as used herein, are broad terms and are used in their ordinary sense, including, without limitation, one or more components being linked to another component(s) in a manner that allows transmission of signals between the components. For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal; the signal can then be transmitted to an electronic circuit. In this case, the electrode is “operably linked” to the electronic circuitry. These terms are broad enough to include wireless connectivity.
The term “electronic circuitry,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the components of a device configured to process biological information obtained from a host. In the case of a glucose-measuring device, the biological information is obtained by a sensor regarding a particular glucose in a biological fluid, thereby providing data regarding the amount of that glucose in the fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398, which are hereby incorporated by reference, describe suitable electronic circuits that can be utilized with devices including the biointerface membrane of a preferred embodiment.
The term “substantially” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, being largely but not necessarily wholly that which is specified.
The term “proximal” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, near to a point of reference such as an origin, a point of attachment, or the midline of the body. For example, in some embodiments of a glucose sensor, wherein the glucose sensor is the point of reference, an oxygen sensor located proximal to the glucose sensor will be in contact with or nearby the glucose sensor such that their respective local environments are shared (e.g., levels of glucose, oxygen, pH, temperature, etc. are similar).
The term “distal” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, spaced relatively far from a point of reference, such as an origin or a point of attachment, or midline of the body. For example, in some embodiments of a glucose sensor, wherein the glucose sensor is the point of reference, an oxygen sensor located distal to the glucose sensor will be sufficiently far from the glucose sensor such their respective local environments are not shared (e.g., levels of glucose, oxygen, pH, temperature, etc. may not be similar).
The term “electrochemical cell,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a device in which chemical energy is converted to electrical energy. Such a cell typically consists of two or more electrodes held apart from each other and in contact with an electrolyte solution. Connection of the electrodes to a source of direct electric current renders one of them negatively charged and the other positively charged. Positive ions in the electrolyte migrate to the negative electrode (cathode) and there combine with one or more electrons, losing part or all of their charge and becoming new ions having lower charge or neutral atoms or molecules; at the same time, negative ions migrate to the positive electrode (anode) and transfer one or more electrons to it, also becoming new ions or neutral particles. The overall effect of the two processes is the transfer of electrons from the negative ions to the positive ions, a chemical reaction.
The term “potentiostat,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, an electrical system that controls the potential between the working and reference electrodes of a three-electrode cell at a preset value. It forces whatever current is necessary to flow between the working and counter electrodes to keep the desired potential, as long as the needed cell voltage and current do not exceed the compliance limits of the potentiostat.
The term “electrical potential,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the electrical potential difference between two points in a circuit which is the cause of the flow of a current.
The term “host,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, mammals, particularly humans.
The phrase “continuous glucose sensing,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the period in which monitoring of plasma glucose concentration is continuously or continually performed, for example, at time intervals ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes, or longer.
The term “sensor head,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the region of a monitoring device responsible for the detection of a particular glucose. The sensor head generally comprises a non-conductive body, a working electrode (anode), a reference electrode and a counter electrode (cathode) passing through and secured within the body forming an electrochemically reactive surface at one location on the body and an electronic connection at another location on the body, and a multi-region membrane affixed to the body and covering the electrochemically reactive surface. The counter electrode typically has a greater electrochemically reactive surface area than the working electrode. During general operation of the sensor a biological sample (e.g., blood or interstitial fluid) or a portion thereof contacts (directly or after passage through one or more membranes or domains) an enzyme (e.g., glucose oxidase); the reaction of the biological sample (or portion thereof) results in the formation of reaction products that allow a determination of the glucose level in the biological sample. In some embodiments, the multi-region membrane includes an enzyme domain (e.g., glucose oxidase), and an electrolyte phase (e.g., a free-flowing liquid phase comprising an electrolyte-containing fluid, as described further below).
The term “electrochemically reactive surface,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the surface of an electrode where an electrochemical reaction takes place. In the case of the working electrode, the hydrogen peroxide produced by the enzyme catalyzed reaction of the glucose being detected reacts creating a measurable electronic current (e.g., detection of glucose utilizing glucose oxidase produces H2O2 as a by product, H2O2 reacts with the surface of the working electrode producing two protons (2H+), two electrons (2e−) and one molecule of oxygen (O2) which produces the electronic current being detected). In the case of the counter electrode, a reducible species, e.g., O2 is reduced at the electrode surface in order to balance the current being generated by the working electrode.
The term “electronic connection,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, any electronic connection known to those in the art that can be utilized to interface the sensor head electrodes with the electronic circuitry of a device such as mechanical (e.g., pin and socket) or soldered.
The terms “operably connected” and “operably linked,” as used herein, are broad terms and are used in their ordinary sense, including, without limitation, one or more components being linked to another component(s) in a manner that allows transmission of signals between the components, e.g., wired or wirelessly. For example, one or more electrodes can be used to detect the amount of analyte in a sample and convert that information into a signal; the signal can then be transmitted to an electronic circuit means. In this case, the electrode is “operably linked” to the electronic circuitry.
The term “sensing membrane,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a permeable or semi-permeable membrane that can be comprised of two or more domains and is typically constructed of materials of a few microns thickness or more, which are permeable to oxygen and may or may not be permeable to glucose. In one example, the sensing membrane comprises an immobilized glucose oxidase enzyme, which enables an electrochemical reaction to occur to measure a concentration of glucose.
The term “biointerface membrane,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a permeable membrane that can be comprised of two or more domains and is typically constructed of materials of a few microns thickness or more, which can be placed over the sensor body to keep host cells (e.g., macrophages) from gaining proximity to, and thereby damaging, the sensing membrane or forming a barrier cell layer and interfering with the transport of glucose across the tissue-device interface.
The term “Clarke Error Grid,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, an error grid analysis, which evaluates the clinical significance of the difference between a reference glucose value and a sensor generated glucose value, taking into account 1) the value of the reference glucose measurement, 2) the value of the sensor glucose measurement, 3) the relative difference between the two values, and 4) the clinical significance of this difference. See Clarke et al., “Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose,” Diabetes Care, Volume 10, Number 5, September-October 1987, which is incorporated by reference herein in its entirety.
The term “physiologically feasible,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the physiological parameters obtained from continuous studies of glucose data in humans and/or animals. For example, a maximal sustained rate of change of glucose in humans of about 4 to 5 mg/dL/min and a maximum acceleration of the rate of change of about 0.1 to 0.2 mg/dL/min/min are deemed physiologically feasible limits. Values outside of these limits would be considered non-physiological and likely a result of signal error, for example. As another example, the rate of change of glucose is lowest at the maxima and minima of the daily glucose range, which are the areas of greatest risk in patient treatment, thus a physiologically feasible rate of change can be set at the maxima and minima based on continuous studies of glucose data. As a further example, it has been observed that the best solution for the shape of the curve at any point along glucose signal data stream over a certain time period (e.g., about 20 to 30 minutes) is a straight line, which can be used to set physiological limits.
The term “ischemia,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, local and temporary deficiency of blood supply due to obstruction of circulation to a part (e.g., sensor). Ischemia can be caused by mechanical obstruction (e.g., arterial narrowing or disruption) of the blood supply, for example.
The term “system noise,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, unwanted electronic or diffusion-related noise which can include Gaussian, motion-related, flicker, kinetic, or other white noise, for example.
The terms “signal artifacts” and “transient non-glucose related signal artifacts that have a higher amplitude than system noise,” as used herein, are broad terms and are used in their ordinary sense, including, without limitation, signal noise that is caused by substantially non-glucose reaction rate-limiting phenomena, such as ischemia, pH changes, temperature changes, pressure, and stress, for example. Signal artifacts, as described herein, are typically transient and characterized by a higher amplitude than system noise.
The terms “low noise,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, noise that substantially decreases signal amplitude.
The terms “high noise” and “high spikes,” as used herein, are broad terms and are used in their ordinary sense, including, without limitation, noise that substantially increases signal amplitude.
The term “frequency content,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the spectral density, including the frequencies contained within a signal and their power.
The term “spectral density,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, power spectral density of a given bandwidth of electromagnetic radiation is the total power in this bandwidth divided by the specified bandwidth. Spectral density is usually expressed in Watts per Hertz (W/Hz).
The term “orthogonal transform,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a general integral transform that is defined by g(α)=∫abƒ(t)K(α,t)dt, where K(α,t) represents a set of orthogonal basis functions.
The term “Fourier Transform,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a technique for expressing a waveform as a weighted sum of sines and cosines.
The term “Discrete Fourier Transform,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a specialized Fourier transform where the variables are discrete.
The term “wavelet transform,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a transform which converts a signal into a series of wavelets, which in theory allows signals processed by the wavelet transform to be stored more efficiently than ones processed by Fourier transform. Wavelets can also be constructed with rough edges, to better approximate real-world signals.
The term “chronoamperometry,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, an electrochemical measuring technique used for electrochemical analysis or for the determination of the kinetics and mechanism of electrode reactions. A fast-rising potential pulse is enforced on the working (or reference) electrode of an electrochemical cell and the current flowing through this electrode is measured as a function of time.
The term “pulsed amperometric detection,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, an electrochemical flow cell and a controller, which applies the potentials and monitors current generated by the electrochemical reactions. The cell can include one or multiple working electrodes at different applied potentials. Multiple electrodes can be arranged so that they face the chromatographic flow independently (parallel configuration), or sequentially (series configuration).
The term “linear regression,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, finding a line in which a set of data has a minimal measurement from that line. Byproducts of this algorithm include a slope, a y-intercept, and an R-Squared value that determine how well the measurement data fits the line.
The term “non-linear regression,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, fitting a set of data to describe the relationship between a response variable and one or more explanatory variables in a non-linear fashion.
The term “mean,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, the sum of the observations divided by the number of observations.
The term “trimmed mean,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, a mean taken after extreme values in the tails of a variable (e.g., highs and lows) are eliminated or reduced (e.g., “trimmed”). The trimmed mean compensates for sensitivities to extreme values by dropping a certain percentage of values on the tails. For example, the 50% trimmed mean is the mean of the values between the upper and lower quartiles. The 90% trimmed mean is the mean of the values after truncating the lowest and highest 5% of the values. In one example, two highest and two lowest measurements are removed from a data set and then the remaining measurements are averaged.
The term “non-recursive filter,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, an equation that uses moving averages as inputs and outputs.
The terms “recursive filter” and “auto-regressive algorithm,” as used herein, are broad terms and are used in their ordinary sense, including, without limitation, an equation in which includes previous averages are part of the next filtered output. More particularly, the generation of a series of observations whereby the value of each observation is partly dependent on the values of those that have immediately preceded it. One example is a regression structure in which lagged response values assume the role of the independent variables.
The term “signal estimation algorithm factors,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, one or more algorithms that use historical and/or present signal data stream values to estimate unknown signal data stream values. For example, signal estimation algorithm factors can include one or more algorithms, such as linear or non-linear regression. As another example, signal estimation algorithm factors can include one or more sets of coefficients that can be applied to one algorithm.
As employed herein, the following abbreviations apply: Eq and Eqs (equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM (micromolar); N (Normal); mol (moles); mmol (millimoles); μmol (micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg (micrograms); Kg (kilograms); L (liters); mL (milliliters); dL (deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm (micrometers); nm (nanometers); h and hr (hours); min. (minutes); s and sec. (seconds); ° C. (degrees Centigrade).
Overview
The preferred embodiments relate to the use of a glucose sensor that measures a concentration of glucose or a substance indicative of the concentration or presence of the glucose. In some embodiments, the glucose sensor is a continuous device, for example a subcutaneous, transdermal, or intravascular device. In some embodiments, the device can analyze a plurality of intermittent blood samples. The glucose sensor can use any method of glucose-measurement, including enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, or the like.
The glucose sensor can use any known method, including invasive, minimally invasive, and non-invasive sensing techniques, to provide a data stream indicative of the concentration of glucose in a host. The data stream is typically a raw data signal that is used to provide a useful value of glucose to a user, such as a patient or doctor, who may be using the sensor. It is well known that raw data streams typically include system noise such as defined herein; however the preferred embodiments address the detection and replacement of “signal artifacts” as defined herein. Accordingly, appropriate signal estimation (e.g., filtering, data smoothing, augmenting, projecting, and/or other methods) replace such erroneous signals (e.g., signal artifacts) in the raw data stream.
Glucose Sensor
The glucose sensor can be any device capable of measuring the concentration of glucose. One exemplary embodiment is described below, which utilizes an implantable glucose sensor. However, it should be understood that the devices and methods described herein can be applied to any device capable of detecting a concentration of glucose and providing an output signal that represents the concentration of glucose.
In one embodiment, the three electrodes 16, which protrude through the head 14, include a platinum working electrode, a platinum counter electrode, and a silver/silver chloride reference electrode. The top ends of the electrodes are in contact with an electrolyte phase (not shown), which is a free-flowing fluid phase disposed between the sensing membrane 17 and the electrodes 16. The sensing membrane 17 includes an enzyme, e.g., glucose oxidase, which covers the electrolyte phase. The biointerface membrane 18 covers the sensing membrane 17 and serves, at least in part, to protect the sensor 10 from external forces that can result in environmental stress cracking of the sensing membrane 17.
In the illustrated embodiment, the counter electrode is provided to balance the current generated by the species being measured at the working electrode. In the case of a glucose oxidase based glucose sensor, the species being measured at the working electrode is H2O2. Glucose oxidase catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconate according to the following reaction:
Glucose+O2→Gluconate+H2O2
The change in H2O2 can be monitored to determine glucose concentration because for each glucose molecule metabolized, there is a proportional change in the product H2O2. Oxidation of H2O2 by the working electrode is balanced by reduction of ambient oxygen, enzyme generated H2O2, or other reducible species at the counter electrode. The H2O2 produced from the glucose oxidase reaction further reacts at the surface of working electrode and produces two protons (2H+), two electrons (2e−), and one oxygen molecule (O2).
In one embodiment, a potentiostat is employed to monitor the electrochemical reaction at the electrochemical cell. The potentiostat applies a constant potential to the working and reference electrodes to determine a current value. The current that is produced at the working electrode (and flows through the circuitry to the counter electrode) is proportional to the amount of H2O2 that diffuses to the working electrode. Accordingly, a raw signal can be produced that is representative of the concentration of glucose in the user's body, and therefore can be utilized to estimate a meaningful glucose value, such as described herein.
One problem with raw data stream output of enzymatic glucose sensors such as described above is caused by transient non-glucose reaction rate-limiting phenomenon. For example, if oxygen is deficient, relative to the amount of glucose, then the enzymatic reaction will be limited by oxygen rather than glucose. Consequently, the output signal will be indicative of the oxygen concentration rather than the glucose concentration, producing erroneous signals. Other non-glucose reaction rate-limiting phenomenon could include temperature and/or pH changes, for example. Accordingly, reduction of signal noise, and particularly replacement of transient non-glucose related signal artifacts in the data stream that have a higher amplitude than system noise, can be performed in the sensor and/or in the receiver, such as described in more detail below in the sections entitled “Signal Artifacts Detection” and “Signal Artifacts Replacement.”
A microprocessor 22 is the central control unit that houses EEPROM 23 and SRAM 24, and controls the processing of the sensor electronics. It is noted that certain alternative embodiments can utilize a computer system other than a microprocessor to process data as described herein. In other alternative embodiments, an application-specific integrated circuit (ASIC) can be used for some or all the sensor's central processing. The EEPROM 23 provides semi-permanent storage of data, for example, storing data such as sensor identifier (ID) and programming to process data streams (e.g., programming for signal artifacts detection and/or replacement such as described elsewhere herein). The SRAM 24 can be used for the system's cache memory, for example for temporarily storing recent sensor data.
A battery 25 is operatively connected to the microprocessor 22 and provides the necessary power for the sensor 10. In one embodiment, the battery is a Lithium Manganese Dioxide battery, however any appropriately sized and powered battery can be used (e.g., AAA, Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metal hydride, Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, or hermetically-sealed). In some embodiments the battery is rechargeable. In some embodiments, a plurality of batteries can be used to power the system. A Quartz Crystal 26 is operatively connected to the microprocessor 22 and maintains system time for the computer system as a whole.
An RF Transceiver 27 is operably connected to the microprocessor 22 and transmits the sensor data from the sensor 10 to a receiver (see
In some embodiments, a Signal Artifacts Detector 29 includes one or more of the following: an oxygen detector 29a, a pH detector 29b, a temperature detector 29c, and a pressure/stress detector 29d, which is described in more detail with reference to signal artifacts detection. It is noted that in some embodiments the signal artifacts detector 29 is a separate entity (e.g., temperature detector) operatively connected to the microprocessor, while in other embodiments, the signal artifacts detector is a part of the microprocessor and utilizes readings from the electrodes, for example, to detect ischemia and other signal artifacts.
Receiver
In some embodiments, a user can toggle through some or all of the screens shown in
A quartz crystal 40 is operatively connected to an RF transceiver 41 that together function to receive and synchronize data streams (e.g., raw data streams transmitted from the RF transceiver). Once received, a microprocessor 42 processes the signals, such as described below.
The microprocessor 42 is the central control unit that provides the processing, such as calibration algorithms stored within EEPROM 43. The EEPROM 43 is operatively connected to the microprocessor 42 and provides semi-permanent storage of data, storing data such as receiver ID and programming to process data streams (e.g., programming for performing calibration and other algorithms described elsewhere herein). SRAM 44 is used for the system's cache memory and is helpful in data processing.
A battery 45 is operatively connected to the microprocessor 42 and provides power for the receiver. In one embodiment, the battery is a standard AAA alkaline battery, however any appropriately sized and powered battery can be used. In some embodiments, a plurality of batteries can be used to power the system. A quartz crystal 46 is operatively connected to the microprocessor 42 and maintains system time for the computer system as a whole.
A user interface 47 comprises a keyboard 2, speaker 3, vibrator 4, backlight 5, liquid crystal display (LCD 6), and one or more buttons 7. The components that comprise the user interface 47 provide controls to interact with the user. The keyboard 2 can allow, for example, input of user information about himself/herself, such as mealtime, exercise, insulin administration, and reference glucose values. The speaker 3 can provide, for example, audible signals or alerts for conditions such as present and/or predicted hyper- and hypoglycemic conditions. The vibrator 4 can provide, for example, tactile signals or alerts for reasons such as described with reference to the speaker, above. The backlight 5 can be provided, for example, to aid the user in reading the LCD in low light conditions. The LCD 6 can be provided, for example, to provide the user with visual data output such as is illustrated in
Communication ports, including a PC communication (com) port 48 and a reference glucose monitor com port 49 can be provided to enable communication with systems that are separate from, or integral with, the receiver 30. The PC com port 48, for example, a serial communications port, allows for communicating with another computer system (e.g., PC, PDA, server, or the like). In one exemplary embodiment, the receiver 30 is able to download historical data to a physician's PC for retrospective analysis by the physician. The reference glucose monitor com port 49 allows for communicating with a reference glucose monitor (not shown) so that reference glucose values can be downloaded into the receiver 30, for example, automatically. In one embodiment, the reference glucose monitor is integral with the receiver 30, and the reference glucose com port 49 allows internal communication between the two integral systems. In another embodiment, the reference glucose monitor com port 49 allows a wireless or wired connection to reference glucose monitor such as a self-monitoring blood glucose monitor (e.g., for measuring finger stick blood samples).
Calibration
Reference is now made to
Calibration of the glucose sensor 10 comprises data processing that converts a sensor data stream into an estimated glucose measurement that is meaningful to a user. Accordingly, a reference glucose value can be used to calibrate the data stream from the glucose sensor 10.
At block 51, a sensor data receiving module, also referred to as the sensor data module, receives sensor data (e.g., a data stream), including one or more time-spaced sensor data points, from a sensor via the receiver, which can be in wired or wireless communication with the sensor. Some or all of the sensor data point(s) can be replaced by estimated signal values to address signal noise such as described in more detail elsewhere herein. It is noted that during the initialization of the sensor, prior to initial calibration, the receiver 30 (e.g., computer system) receives and stores the sensor data, however it may not display any data to the user until initial calibration and eventually stabilization of the sensor 10 has been determined.
At block 52, a reference data receiving module, also referred to as the reference input module, receives reference data from a reference glucose monitor, including one or more reference data points. In one embodiment, the reference glucose points can comprise results from a self-monitored blood glucose test (e.g., from a finger stick test). In one such embodiment, the user can administer a self-monitored blood glucose test to obtain glucose value (e.g., point) using any known glucose sensor, and enter the numeric glucose value into the computer system. In another such embodiment, a self-monitored blood glucose test comprises a wired or wireless connection to the receiver 30 (e.g. computer system) so that the user simply initiates a connection between the two devices, and the reference glucose data is passed or downloaded between the self-monitored blood glucose test and the receiver 30. In yet another such embodiment, the self-monitored glucose test is integral with the receiver 30 so that the user simply provides a blood sample to the receiver 30, and the receiver 30 runs the glucose test to determine a reference glucose value.
Certain acceptability parameters can be set for reference values received from the user. For example, in one embodiment, the receiver may only accept reference glucose values between about 40 and about 400 mg/dL.
At block 53, a data matching module, also referred to as the processor module, matches reference data (e.g., one or more reference glucose data points) with substantially time corresponding sensor data (e.g., one or more sensor data points) to provide one or more matched data pairs. In one embodiment, one reference data point is matched to one time corresponding sensor data point to form a matched data pair. In another embodiment, a plurality of reference data points are averaged (e.g., equally or non-equally weighted average, mean-value, median, or the like) and matched to one time corresponding sensor data point to form a matched data pair. In another embodiment, one reference data point is matched to a plurality of time corresponding sensor data points averaged to form a matched data pair. In yet another embodiment, a plurality of reference data points are averaged and matched to a plurality of time corresponding sensor data points averaged to form a matched data pair.
In one embodiment, a time corresponding sensor data comprises one or more sensor data points that occur, for example, 15±5 min after the reference glucose data timestamp (e.g., the time that the reference glucose data is obtained). In this embodiment, the 15 minute time delay has been chosen to account for an approximately 10 minute delay introduced by the filter used in data smoothing and an approximately 5 minute physiological time-lag (e.g., the time necessary for the glucose to diffusion through a membrane(s) of an glucose sensor). In alternative embodiments, the time corresponding sensor value can be more or less than in the above-described embodiment, for example ±60 minutes. Variability in time correspondence of sensor and reference data can be attributed to, for example, a longer or shorter time delay introduced during signal estimation, or if the configuration of the glucose sensor 10 incurs a greater or lesser physiological time lag.
In some practical implementations of the sensor 10, the reference glucose data can be obtained at a time that is different from the time that the data is input into the receiver 30. Accordingly, it should be noted that the “time stamp” of the reference glucose (e.g., the time at which the reference glucose value was obtained) may not be the same as the time at which the receiver 30 obtained the reference glucose data. Therefore, some embodiments include a time stamp requirement that ensures that the receiver 30 stores the accurate time stamp for each reference glucose value, that is, the time at which the reference value was actually obtained from the user.
In some embodiments, tests are used to evaluate the best-matched pair using a reference data point against individual sensor values over a predetermined time period (e.g., about 30 minutes). In one such embodiment, the reference data point is matched with sensor data points at 5-minute intervals and each matched pair is evaluated. The matched pair with the best correlation can be selected as the matched pair for data processing. In some alternative embodiments, matching a reference data point with an average of a plurality of sensor data points over a predetermined time period can be used to form a matched pair.
At block 54, a calibration set module, also referred to as the processor module, forms an initial calibration set from a set of one or more matched data pairs, which are used to determine the relationship between the reference glucose data and the sensor glucose data, such as described in more detail with reference to block 55, below.
The matched data pairs, which make up the initial calibration set, can be selected according to predetermined criteria. In some embodiments, the number (n) of data pair(s) selected for the initial calibration set is one. In other embodiments, n data pairs are selected for the initial calibration set wherein n is a function of the frequency of the received reference data points. In one exemplary embodiment, six data pairs make up the initial calibration set.
In some embodiments, the data pairs are selected only within a certain glucose value threshold, for example wherein the reference glucose value is between about 40 and about 400 mg/dL. In some embodiments, the data pairs that form the initial calibration set are selected according to their time stamp.
At block 55, the conversion function module uses the calibration set to create a conversion function. The conversion function substantially defines the relationship between the reference glucose data and the glucose sensor data. A variety of known methods can be used with the preferred embodiments to create the conversion function from the calibration set. In one embodiment, wherein a plurality of matched data points form the initial calibration set, a linear least squares regression is performed on the initial calibration set such as described in more detail with reference to
At block 56, a sensor data transformation module uses the conversion function to transform sensor data into substantially real-time glucose value estimates, also referred to as calibrated data, as sensor data is continuously (or intermittently) received from the sensor. In other words, the offset value at any given point in time can be subtracted from the raw value (e.g., in counts) and divided by the slope to obtain the estimated glucose value:
In some alternative embodiments, the sensor and/or reference glucose values are stored in a database for retrospective analysis.
At block 57, an output module provides output to the user via the user interface. The output is representative of the estimated glucose value, which is determined by converting the sensor data into a meaningful glucose value such as described in more detail with reference to block 56, above. User output can be in the form of a numeric estimated glucose value, an indication of directional trend of glucose concentration, and/or a graphical representation of the estimated glucose data over a period of time, for example. Other representations of the estimated glucose values are also possible, for example audio and tactile.
In one embodiment, such as shown in
Accordingly, after initial calibration of the sensor, real-time continuous glucose information can be displayed on the user interface so that the user can regularly and proactively care for his/her diabetic condition within the bounds set by his/her physician.
In alternative embodiments, the conversion function is used to predict glucose values at future points in time. These predicted values can be used to alert the user of upcoming hypoglycemic or hyperglycemic events. Additionally, predicted values can be used to compensate for the time lag (e.g., 15 minute time lag such as described elsewhere herein), so that an estimated glucose value displayed to the user represents the instant time, rather than a time delayed estimated value.
In some embodiments, the substantially real-time estimated glucose value, a predicted future estimated glucose value, a rate of change, and/or a directional trend of the glucose concentration is used to control the administration of a constituent to the user, including an appropriate amount and time, in order to control an aspect of the user's biological system. One such example is a closed loop glucose sensor and insulin pump, wherein the glucose data (e.g., estimated glucose value, rate of change, and/or directional trend) from the glucose sensor is used to determine the amount of insulin, and time of administration, that can be given to a diabetic user to evade hyper- and hypoglycemic conditions.
In alternative embodiments, other algorithms could be used to determine the conversion function, for example forms of linear and non-linear regression, for example fuzzy logic, neural networks, piece-wise linear regression, polynomial fit, genetic algorithms, and other pattern recognition and signal estimation techniques.
In yet other alternative embodiments, the conversion function can comprise two or more different optimal conversions because an optimal conversion at any time is dependent on one or more parameters, such as time of day, calories consumed, exercise, or glucose concentration above or below a set threshold, for example. In one such exemplary embodiment, the conversion function is adapted for the estimated glucose concentration (e.g., high vs. low). For example in an implantable glucose sensor it has been observed that the cells surrounding the implant will consume at least a small amount of glucose as it diffuses toward the glucose sensor. Assuming the cells consume substantially the same amount of glucose whether the glucose concentration is low or high, this phenomenon will have a greater effect on the concentration of glucose during low blood sugar episodes than the effect on the concentration of glucose during relatively higher blood sugar episodes. Accordingly, the conversion function can be adapted to compensate for the sensitivity differences in blood sugar level. In one implementation, the conversion function comprises two different regression lines, wherein a first regression line is applied when the estimated blood glucose concentration is at or below a certain threshold (e.g., 150 mg/dL) and a second regression line is applied when the estimated blood glucose concentration is at or above a certain threshold (e.g., 150 mg/dL). In one alternative implementation, a predetermined pivot of the regression line that forms the conversion function can be applied when the estimated blood is above or below a set threshold (e.g., 150 mg/dL), wherein the pivot and threshold are determined from a retrospective analysis of the performance of a conversion function and its performance at a range of glucose concentrations. In another implementation, the regression line that forms the conversion function is pivoted about a point in order to comply with clinical acceptability standards (e.g., Clarke Error Grid, Consensus Grid, mean absolute relative difference, or other clinical cost function). Although only a few example implementations are described, other embodiments include numerous implementations wherein the conversion function is adaptively applied based on one or more parameters that can affect the sensitivity of the sensor data over time.
Additional methods for processing sensor glucose data are disclosed in copending U.S. patent application Ser. No. 10/633,367 filed Aug. 1, 2003 and entitled, “SYSTEM AND METHODS FOR PROCESSING ANALYTE SENSOR DATA,” which is incorporated herein by reference in its entirety. In view of the above-described data processing, it should be obvious that improving the accuracy of the data stream will be advantageous for improving output of glucose sensor data. Accordingly, the following description is related to improving data output by decreasing signal artifacts on the raw data stream from the sensor. The data smoothing methods of preferred embodiments can be employed in conjunction with any sensor or monitor measuring levels of an analyte in vivo, wherein the level of the analyte fluctuates over time, including but not limited to such sensors as described in U.S. Pat. No. 6,001,067 to Shults et al.; U.S. Patent Application 2003/0023317 to Brauker et al.; U.S. Pat. No. 6,212,416 to Ward et al.; U.S. Pat. No. 6,119,028 to Schulman et al; U.S. Pat. No. 6,400,974 to Lesho; U.S. Pat. No. 6,595,919 to Berner et al.; U.S. Pat. No. 6,141,573 to Kurnik et al.; U.S. Pat. No. 6,122,536 to Sun et al.; European Patent Application EP 1153571 to Varall et al.; U.S. Pat. No. 6,512,939 to Colvin et al.; U.S. Pat. No. 5,605,152 to Slate et al.; U.S. Pat. No. 4,431,004 to Bessman et al.; U.S. Pat. No. 4,703,756 to Gough et al; U.S. Pat. No. 6,514,718 to Heller et al; and U.S. Pat. to U.S. Pat. No. 5,985,129 to Gough et al., each of which are incorporated in there entirety herein by reference.
Signal Artifacts
Typically, a glucose sensor produces a data stream that is indicative of the glucose concentration of a host, such as described in more detail above. However, it is well known that the above described glucose sensor is only one example of an abundance of glucose sensors that are able to provide raw data output indicative of the concentration of glucose. Thus, it should be understood that the systems and methods described herein, including signal artifacts detection, signal artifacts replacement, and other data processing, can be applied to a data stream obtained from any glucose sensor.
Raw data streams typically have some amount of “system noise,” caused by unwanted electronic or diffusion-related noise that degrades the quality of the signal and thus the data. Accordingly, conventional glucose sensors are known to smooth raw data using methods that filter out this system noise, and the like, in order to improve the signal to noise ratio, and thus data output. One example of a conventional data-smoothing algorithm includes a finite impulse response filter (FIR), which is particularly suited for reducing high-frequency noise (see Steil et al. U.S. Pat. No. 6,558,351).
The “system noise” such as shown in sections 72a, 72b of the data streams of
The “signal artifacts” such as shown in sections 74a, 74b of the data stream of
Analysis of signal artifacts such as shown sections 74a, 74b of
In another example of non-glucose reaction rate-limiting phenomena, skin temperature can vary dramatically, which can result in thermally related erosion of the signal (e.g., temperature changes between 32 and 39 degrees Celsius have been measured in humans). In yet another embodiment, wherein the glucose sensor is placed intravenously, increased impedance can result from the sensor resting against wall of the blood vessel, for example, producing this non-glucose reaction rate-limiting noise due to oxygen deficiency.
Because signal artifacts are not mere system noise, but rather are caused by specific rate-limiting mechanisms, methods used for conventional random noise filtration produce data lower (or in some cases higher) than the actual blood glucose levels due to the expansive nature of these signal artifacts. To overcome this, the preferred embodiments provide systems and methods for replacing at least some of the signal artifacts by estimating glucose signal values.
At block 82, a sensor data receiving module, also referred to as the sensor data module 82, receives sensor data (e.g., a data stream), including one or more time-spaced sensor 10 data points. In some embodiments, the data stream is stored in the sensor for additional processing; in some alternative embodiments, the sensor 10 periodically transmits the data stream to the receiver 30, which can be in wired or wireless communication with the sensor.
At block 84, a signal artifacts detection module, also referred to as the signal artifacts detector 84, is programmed to detect transient non-glucose related signal artifacts in the data stream that have a higher amplitude than system noise, such as described in more detail with reference to
At block 86, the signal artifacts replacement module, also referred to as the signal estimation module, replaces some or an entire data stream with estimated glucose signal values using signal estimation. Numerous embodiments for performing signal estimation are described in more detail in the section entitled “Signal Artifacts Replacement,” all of which are encompassed by the signal artifacts replacement module, block 86. It is noted that in some embodiments, signal estimation/replacement is initiated in response to positive detection of signal artifacts on the data stream, and subsequently stopped in response to detection of negligible signal artifacts on the data stream. In some embodiments, the system waits a predetermined time period (e.g., between 30 seconds and 30 minutes) before switching the signal estimation on or off to ensure that a consistent detection has been ascertained. In some embodiments, however, signal estimation/replacement can continuously or continually run.
Some embodiments of signal estimation can additionally include discarding data that is considered sufficiently unreliable and/or erroneous such that the data should not be used in a signal estimation algorithm. In these embodiments, the system can be programmed to discard outlier data points, for example data points that are so extreme that they can skew the data even with the most comprehensive filtering or signal estimation, and optionally replace those points with a projected value based on historical data or present data (e.g., linear regression, recursive filtering, or the like). One example of discarding sensor data includes discarding sensor data that falls outside of a “Cone of Possibility” such as described in more detail elsewhere herein. Another example includes discarding sensor data when signal artifacts detection detects values outside of a predetermined threshold (e.g., oxygen concentration below a set threshold, temperature above a certain threshold, signal amplitude above a certain threshold, etc). Any of the signal estimation/replacement algorithms described herein can then be used to project data values for those data that were discarded.
Signal Artifacts Detection
Analysis of signals from glucose sensors indicates at least two types of noise, which are characterized herein as 1) system noise and 2) signal artifacts, such as described in more detail above. It is noted that system noise is easily smoothed using the algorithms provided herein; however, the systems and methods described herein particularly address signal artifacts, by replacing transient erroneous signal noise caused by rate-limiting phenomenon with estimated signal values.
In certain embodiments of signal artifacts detection, oxygen monitoring is used to detect whether transient non-glucose dependent signal artifacts due to ischemia. Low oxygen concentrations in or near the glucose sensor can account for a large part of the transient non-glucose related signal artifacts as defined herein on a glucose sensor signal, particularly in subcutaneously implantable glucose sensors. Accordingly, detecting oxygen concentration, and determining if ischemia exists can discover ischemia-related signal artifacts. A variety of methods can be used to test for oxygen. For example, an oxygen-sensing electrode, or other oxygen sensor can be employed. The measurement of oxygen concentration can be sent to a microprocessor, which determines if the oxygen concentration indicates ischemia.
In some embodiments of ischemia detection, an oxygen sensor is placed proximal to or within the glucose sensor. For example, the oxygen sensor can be located on or near the glucose sensor such that their respective local environments are shared and oxygen concentration measurement from the oxygen sensor represents an accurate measurement of the oxygen concentration on or within the glucose sensor. In some alternative embodiments of ischemia detection, an oxygen sensor is also placed distal to the glucose sensor. For example, the oxygen sensor can be located sufficiently far from the glucose sensor such that their respective local environments are not shared and oxygen measurements from the proximal and distal oxygen sensors can be compared to determine the relative difference between the respective local environments. By comparing oxygen concentration proximal and distal oxygen sensor, change in local (proximal) oxygen concentration can be determined from a reference (distal) oxygen concentration.
Oxygen sensors are useful for a variety of purposes. For example, U.S. Pat. No. 6,512,939 to Colvin et al., which is incorporated herein by reference, discloses an oxygen sensor that measures background oxygen levels. However, Colvin et al. rely on the oxygen sensor for the data stream of glucose measurements by subtraction of oxygen remaining after exhaustion of glucose by an enzymatic reaction from total unreacted oxygen concentration.
In another embodiment of ischemia detection, wherein the glucose sensor is an electrochemical sensor that includes a potentiostat, pulsed amperometric detection can be employed to determine an oxygen measurement. Pulsed amperometric detection includes switching, cycling, or pulsing the voltage of the working electrode (or reference electrode) in an electrochemical system, for example between a positive voltage (e.g., +0.6 for detecting glucose) and a negative voltage (e.g., −0.6 for detecting oxygen). U.S. Pat. No. 4,680,268 to Clark, Jr., which is incorporated by reference herein, describes pulsed amperometric detection. In contrast to using signal replacement, Clark, Jr. addresses oxygen deficiency by supplying additional oxygen to the enzymatic reaction.
In another embodiment of ischemia detection, wherein the glucose sensor is an electrochemical sensor and contains a potentiostat, oxygen deficiency can be seen at the counter electrode when insufficient oxygen is available for reduction, which thereby affects the counter electrode in that it is unable to balance the current coming from the working electrode. When insufficient oxygen is available for the counter electrode, the counter electrode can be driven in its electrochemical search for electrons all the way to its most negative value, which could be ground or 0.0V, which causes the reference to shift, reducing the bias voltage such as described in more detail below. In other words, a common result of ischemia will be seen as a drop off in sensor current as a function of glucose concentration (e.g., lower sensitivity). This happens because the working electrode no longer oxidizes all of the H2O2 arriving at its surface because of the reduced bias. In some extreme circumstances, an increase in glucose can produce no increase in current or even a decrease in current.
In another embodiment of ischemia detection, wherein the glucose sensor is an electrochemical sensor and contains a two- or three-cell electrochemical cell, signal artifacts are detected by monitoring the reference electrode. This “reference drift detection” embodiment takes advantage of the fact that the reference electrode will vary or drift in order to maintain a stable bias potential with the working electrode, such as described in more detail herein. This “drifting” generally indicates non-glucose reaction rate-limiting noise, for example due to ischemia. It is noted that the following example describes an embodiment wherein the sensor includes a working, reference, and counter electrodes, such as described in more detail elsewhere herein; however the principles of this embodiment are applicable to a two-cell (e.g., anode and cathode) electrochemical cell as is understood in the art.
In practice, a glucose sensor of one embodiment comprises a membrane that contains glucose oxidase that catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconate, such as described with reference to
One limitation of the electrochemistry is seen when insufficient negative voltage is available to the counter electrode 104 to balance the working electrode 100. This limitation can occur when insufficient oxygen is available to the counter electrode 104 for reduction, for example. When this limitation occurs, the counter electrode can no longer vary its voltage to maintain a balanced current with the working electrode and thus the negative feedback loop 107 used to maintain the reference electrode is compromised. Consequently, the reference electrode voltage will change or “drift,” altering the applied bias potential (i.e., the potential applied between the working and reference electrodes), thereby decreasing the applied bias potential. When this change in applied bias potential occurs, the working electrode can produce erroneous glucose measurements due to either increased or decreased signal strength (ISENSE).
In some embodiments, pH and/or temperature measurements are obtained proximal to the glucose sensor; in some embodiments, pH and/or temperature measurements are also obtained distal to the glucose sensor and the respective measurements compared, such as described in more detail above with reference to oxygen sensors.
In another implementation of signal artifacts detection, wherein temperature is detected, an electronic thermometer can be proximal to or within the glucose sensor, such that the temperature measurement is representative of the temperature of the glucose sensor's local environment. It is noted that accurate sensor function depends on diffusion of molecules from the blood to the interstitial fluid, and then through the membranes of the device to the enzyme membrane. Additionally, diffusion transport of hydrogen peroxide from the enzyme membrane to the electrode occurs. Therefore, temperatures can be a rate determining parameter of diffusion. As temperature decreases, diffusion transport decreases. Under certain human conditions, such as hypothermia or fever, the variations can be considerably greater. Additionally, under normal conditions, the temperature of subcutaneous tissue is known to vary considerably more than core tissues (e.g., core temperature). Temperature thresholds can be set to detect signal artifacts accordingly.
In another implementation, a pH detector is used to detect signal artifacts. In glucose sensors that rely on enzymatic reactions, a pH of the fluid to be sensed can be within the range of about 5.5 to 7.5. Outside of this range, effects may be seen in the enzymatic reaction and therefore data output of the glucose sensor. Accordingly, by detecting if the pH is outside of a predetermined range (e.g., 5.5 to 7.5), a pH detector may detect transient non-glucose related signal artifacts such as described herein. It is noted that the pH threshold can be set at ranges other than provided herein without departing from the preferred embodiments.
In an alternative embodiment of signal artifacts detection, pressure and/or stress can be monitored using known techniques for example by a strain gauge placed on the sensor that detects stress/strain on the circuit board, sensor housing, or other components. A variety of microelectromechanical systems (MEMS) can be utilized to measure pressure and/or stress within the sensor.
In another alternative embodiment of signal artifacts detection, the microprocessor in the sensor (or receiver) periodically evaluates the data stream for high amplitude noise, which is defined by noisy data wherein the amplitude of the noise is above a predetermined threshold. For example, in the graph of
In another alternative embodiment of signal artifacts detection, a method hereinafter referred to as the “Cone of Possibility Detection Method,” utilizes physiological information along with glucose signal values in order define a “cone” of physiologically feasible glucose signal values within a human, such that signal artifacts are detected whenever the glucose signal falls outside of the cone of possibility. Particularly, physiological information depends upon the physiological parameters obtained from continuous studies in the literature as well as our own observations. A first physiological parameter uses a maximal sustained rate of change of glucose in humans (e.g., about 4 to 5 mg/dL/min) and a maximum acceleration of that rate of change (e.g., about 0.1 to 0.2 mg/dL/min2). A second physiological parameter uses the knowledge that rate of change of glucose is lowest at the minima, which is the areas of greatest risk in patient treatment, and the maxima, which has the greatest long-term effect on secondary complications associated with diabetes. A third physiological parameter uses the fact that the best solution for the shape of the curve at any point along the curve over a certain time period (e.g., about 20-30 minutes) is a straight line. Additional physiological parameters can be incorporated and are within the scope of this embodiment.
In practice, the Cone of Possibility Detection Method combines any one or more of the above-described physiological parameters to form an algorithm that defines a cone of possible glucose levels for glucose data captured over a predetermined time period. In one exemplary implementation of the Cone of Possibility Detection Method, the system (microprocessor in the sensor or receiver) calculates a maximum physiological rate of change and determines if the data falls within these physiological limits; if not, signal artifacts are identified. It is noted that the maximum rate of change can be narrowed (e.g., decreased) in some instances. Therefore, additional physiological data could be used to modify the limits imposed upon the Cone of Possibilities Detection Method for sensor glucose values. For example, the maximum per minute rate change can be lower when the subject is sleeping or hasn't eaten in eight hours; on the other hand, the maximum per minute rate change can be higher when the subject is exercising or has consumed high levels of glucose, for example. In general, it has been observed that rates of change are slowest near the maxima and minima of the curve, and that rates of change are highest near the midpoint between the maxima and minima. It should further be noted that rate of change limits are derived from analysis of a range of data significantly higher unsustained rates of change can be observed.
In another alternative embodiment of signal artifacts detection, examination of the spectral content (e.g., frequency content) of the data stream can yield measures useful in detecting signal artifacts. For example, data that has high frequency, and in some cases can be characterized by a large negative slope, are indicative of signal artifacts and can cause sensor signal loss. Specific signal content can be monitored using an orthogonal transform, for example a Fourier transform, a Discrete Fourier Transform (DFT), or any other method known in the art.
The raw data stream in the graph 110 has been adjusted by a linear mapping similar to the calibration algorithm. In this example, the bias (or intercept) has been adjusted but not the proportion (or slope). The slope of the raw data stream would typically be determined by some calibration, but since the calibration has not occurred in this example, the gray levels in the spectrogram 114 indicate relative values. The lower values of the graph 110 are white. They are colored as white below a specific value, highlighting only the most intense areas of the graph.
By monitoring the frequency content 116, high frequency cycles 118 can be observed. The high frequency cycles 118 correspond to signal artifacts 119 such as described herein. Thus, signal artifacts can be detected on a data stream by monitoring frequency content and setting a threshold (e.g., 30 cycles per hour). The set threshold can vary depending on the signal source.
In another alternative embodiment of signal artifacts detection, examination of the signal information content can yield measures useful in detecting signal artifacts. Time series analysis can be used to measure entropy, approximate entropy, variance, and/or percent change of the information content over consecutive windows (e.g., 30 and 60 minute windows of data) of the raw data stream. In one exemplary embodiment, the variance of the raw data signal is measured over 15 minute and 45 minute windows, and signal artifacts are detected when the variance of the data within the 15-minute window exceeds the variance of the data within the 45-minute window.
One or a plurality of the above signal artifacts detection models can be used alone or in combination to detect signal artifacts such as described herein. Accordingly, the data stream associated with the signal artifacts can be discarded, replaced, or otherwise processed in order to reduce or eliminate these signal artifacts and thereby improve the value of the glucose measurements that can be provided to a user.
Signal Artifacts Replacement
Signal Artifacts Replacement, such as described above, can use systems and methods that reduce or replace these signal artifacts that can be characterized by transience, high frequency, high amplitude, and/or substantially non-linear noise. Accordingly, a variety of filters, algorithms, and other data processing are provided that address the detected signal artifacts by replacing the data stream, or portion of the data stream, with estimated glucose signal values. It is noted that “signal estimation” as used herein, is a broad term, which includes filtering, data smoothing, augmenting, projecting, and/or other algorithmic methods that estimate glucose signal values based on present and historical data.
It is noted that a glucose sensor can contain a microprocessor or the like that processes periodically received raw sensor data (e.g., every 30 seconds). Although a data point can be available constantly, for example by use of an electrical integration system in a chemo-electric sensor, relatively frequent (e.g., every 30 seconds), or less frequent data point (e.g., every 5 minutes), can be more than sufficient for patient use. It is noted that accordingly Nyquist Theory, a data point is required about every 10 minutes to accurately describe physiological change in glucose in humans. This represents the lowest useful frequency of sampling. However, it should be recognized that it is desirable to sample more frequently than the Nyquist minimum, to provide for sufficient data in the event that one or more data points are lost, for example. Additionally, more frequently sampled data (e.g., 30-second) can be used to smooth the less frequent data (e.g., 5-minute) that are transmitted. It is noted that in this example, during the course of a 5-minute period, 10 determinations are made at 30-second intervals.
In some embodiments of Signal Artifacts Replacement, signal estimation can be implemented in the sensor and transmitted to a receiver for additional processing. In some embodiments of Signal Artifacts Replacement, raw data can be sent from the sensor to a receiver for signal estimation and additional processing therein. In some embodiments of Signal Artifacts Replacement, signal estimation is performed initially in the sensor, with additional signal estimation in the receiver.
In some embodiments of Signal Artifacts Replacement, wherein the sensor is an implantable glucose sensor, signal estimation can be performed in the sensor to ensure a continuous stream of data. In alternative embodiments, data can be transmitted from the sensor to the receiver, and the estimation performed at the receiver; It is noted however that there can be a risk of transmit-loss in the radio transmission from the sensor to the receiver when the transmission is wireless. For example, in embodiments wherein a sensor is implemented in vivo, the raw sensor signal can be more consistent within the sensor (in vivo) than the raw signal transmitted to a source (e.g., receiver) outside the body (e.g., if a patient were to take the receiver off to shower, communication between the sensor and receiver can be lost and data smoothing in the receiver would halt accordingly). Consequently, It is noted that a multiple point data loss in the filter can take for example, about 25 to about 40 minutes for the data to recover to near where it would have been had there been no data loss.
In some embodiments of Signal Artifacts Replacement, signal estimation is initiated only after signal artifacts are positively detected, and stopped once signal artifacts are negligibly detected. In some alternative embodiments signal estimation is initiated after signal artifacts are positively detected and then stopped after a predetermined time period. In some alternative embodiments, signal estimation can be continuously or continually performed. In some alternative embodiments, one or more forms of signal estimation can be accomplished based on the severity of the signal artifacts, such as will be described with reference the section entitled, “Selective Application of Signal Artifacts Replacement.”
In some embodiments of Signal Artifacts Replacement, the microprocessor performs a linear regression. In one such implementation, the microprocessor performs a linear regression analysis of the n (e.g., 10) most recent sampled sensor values to smooth out the noise. A linear regression averages over a number of points in the time course and thus reduces the influence of wide excursions of any point from the regression line. Linear regression defines a slope and intercept, which is used to generate a “Projected Glucose Value,” which can be used to replace sensor data. This regression can be continually performed on the data stream or continually performed only during the transient signal artifacts. In some alternative embodiments, signal estimation can include non-linear regression.
In another embodiment of Signal Artifacts Replacement, the microprocessor performs a trimmed regression, which is a linear regression of a trimmed mean (e.g., after rejecting wide excursions of any point from the regression line). In this embodiment, after the sensor records glucose measurements at a predetermined sampling rate (e.g., every 30 seconds), the sensor calculates a trimmed mean (e.g., removes highest and lowest measurements from a data set and then regresses the remaining measurements to estimate the glucose value.
In another embodiment of Signal Artifacts Replacement, the microprocessor runs a non-recursive filter, such as a finite impulse response (FIR) filter. A FIR filter is a digital signal filter, in which every sample of output is the weighted sum of past and current samples of input, using only some finite number of past samples.
In another embodiment of Signal Artifacts Replacement, the microprocessor runs a recursive filter, such as an infinite impulse response (IIR) filter. An IIR filter is a type of digital signal filter, in which every sample of output is the weighted sum of past and current samples of input. In one exemplary implementation of an IIR filter, the output is computed using 6 additions/subtractions and 7 multiplications as shown in the following equation:
This polynomial equation includes coefficients that are dependent on sample rate and frequency behavior of the filter. Frequency behavior passes low frequencies up to cycle lengths of 40 minutes, and is based on a 30 second sample rate. In alternative implementations, the sample rate and cycle lengths can be more or less. See Lynn “Recursive Digital Filters for Biological Signals” Med. & Biol. Engineering, Vol. 9, pp. 37-43, which is incorporated herein by reference in its entirety.
To optimize the estimation of the glucose data values, the parameters of the IIR filter can be optimized, for example by increasing or decreasing the cycle lengths (e.g., 10 minutes, 20 minute, 40 minutes, 60 minutes) that are used in the algorithm. Although an increased cycle length can increase the time lag induced by the IIR filter, an increased cycle length can also better estimate glucose data values during severe signal artifacts. In other words, It is noted that the IIR filter, because of its inherent properties, can be particularly suited for noise of a certain amplitude and/or characteristic. In one exemplary embodiment, the IIR filter can be continually applied, however the parameters such as described above can be selectively altered based on the severity of the signal artifacts; in another exemplary embodiment, the IIR filter can be applied only after positive detection of signal artifacts. Selective application of the IIR filter based on the severity of the signal artifacts is described in more detail below with reference to
It is noted that a comparison of linear regression, an FIR filter, and an IIR filter can be advantageous for optimizing their usage in the preferred embodiments. That is, an understanding the design considerations for each algorithm can lead to optimized selection and implementation of the algorithm, as described in the section entitled, “Selective Application of Signal Replacement Algorithms” herein. During system noise, as defined herein, all of the above algorithms can be successfully implemented during system noise with relative ease. During signal artifacts, however, computational efficiency is greater with an IIR-filter as compared with linear regression and FIR-filter. Additionally, although the time lag associated with an IIR filter can be substantially greater than that of the linear regression or FIR-filter, this can be advantageous during severe signal artifacts in order to assign greater weight toward the previous, less noisy data in signal estimation.
In another embodiment of Signal Artifacts Replacement, the microprocessor runs a maximum-average (max-average) filtering algorithm. The max-average algorithm smoothes data based on the discovery that the substantial majority of signal artifacts observed after implantation of glucose sensors in humans, for example, is not distributed evenly above and below the actual blood glucose levels. It has been observed that many data sets are actually characterized by extended periods in which the noise appears to trend downwardly from maximum values with occasional high spikes such as described in more detail above with reference to
The max-average calculation smoothes data at a sampling interval (e.g., every 30 seconds) for transmission to the receiver at a less frequent transmission interval (e.g., every 5 minutes) to minimize the effects of low non-physiological data. First, the microprocessor finds and stores a maximum sensor counts value in a first set of sampled data points (e.g., 5 consecutive, accepted, thirty-second data points). A frame shift time window finds a maximum sensor counts value for each set of sampled data (e.g., each 5-point cycle length) and stores each maximum value. The microprocessor then computes a rolling average (e.g., 5-point average) of these maxima for each sampling interval (e.g., every 30 seconds) and stores these data. Periodically (e.g., every 10th interval), the sensor outputs to the receiver the current maximum of the rolling average (e.g., over the last 10 thirty-second intervals as a smoothed value for that time period (e.g., 5 minutes)). In one example implementation, a 10-point window can be used, and at the 10th interval, the microprocessor computes the average of the most recent 5 or 10 average maxima as the smoothed value for a 5 minute time period.
In some embodiments of the max-average algorithm, an acceptance filter can also be applied to new sensor data to minimize effects of high non-physiological data. In the acceptance filter, each sampled data point (e.g., every 30-seconds) is tested for acceptance into the maximum average calculation. Each new point is compared against the most representative estimate of the sensor curve at the previous sampling interface (e.g., 30-second time point), or at a projection to a current estimated value. To reject high data, the current data point is compared to the most recent value of the average maximum values over a time period (e.g., 5 sampled data points over a 2.5 minute period). If the ratio of current value to the comparison value is greater than a certain threshold (e.g., about 1.02), then the current data point is replaced with a previously accepted value (e.g., 30-second value). If the ratio of current value to the comparison value is in at or within a certain range (e.g., about 1.02 to 0.90), then the current data point is accepted. If the ratio of current value to the comparison value is less than a certain threshold (e.g., about 0.90), then the current data point is replaced with a previously accepted value. The acceptance filter step and max-average calculation are continuously run throughout the data set (e.g., fixed 5-minute windows) on a rolling window basis (e.g., every 30 seconds).
In some implementations of the acceptance filter, the comparison value for acceptance could also be the most recent maximum of 5 accepted sensor points (more sensitive) or the most recent average over 10 averages of 5 maximum values (least sensitive), for example. In some exemplary implementations of the acceptance filter, the projected value for the current time point can be based on regression of the last 4 accepted 30-second values and/or the last 2 to 4 (5 to 15 min) of the 5-minute smoothed values, for example. In some exemplary implementations of the acceptance filter, the percentage comparisons of +2% and −10% of counts value would be replaced by percentage comparisons based on the most recent 24 hour range of counts values; this method would provide improved sensor specificity as compared to a method based on total counts.
In another embodiment of Signal Artifacts Replacement, the microprocessor runs a “Cone of Possibility Replacement Method.” It is noted that this method can be performed in the sensor and/or in the receiver. The Cone of Possibility Detection Method utilizes physiological information along with glucose signal values in order define a “cone” of physiologically feasible glucose signal values within a human. Particularly, physiological information depends upon the physiological parameters obtained from continuous studies in the literature as well as our own observations. A first physiological parameter uses a maximal sustained rate of change of glucose in humans (e.g., about 4 to 5 mg/dl/min) and a maximum sustained acceleration of that rate of change (e.g., about 0.1 to 0.2 mg/min/min). A second physiological parameter uses the knowledge that rate of change of glucose is lowest at the maxima and minima, which are the areas of greatest risk in patient treatment, such as described with reference to Cone of Possibility Detection, above. A third physiological parameter uses the fact that the best solution for the shape of the curve at any point along the curve over a certain time period (e.g., about 20-25 minutes) is a straight line. It is noted that the maximum rate of change can be narrowed in some instances. Therefore, additional physiological data can be used to modify the limits imposed upon the Cone of Possibility Replacement Method for sensor glucose values. For example, the maximum per minute rate change can be lower when the subject is lying down or sleeping; on the other hand, the maximum per minute rate change can be higher when the subject is exercising, for example.
The Cone of Possibility Replacement Method utilizes physiological information along with blood glucose data in order to improve the estimation of blood glucose values within a human in an embodiment of Signal Artifacts Replacement. The Cone of Possibility Replacement Method can be performed on raw data in the sensor, on raw data in the receiver, or on smoothed data (e.g., data that has been replaced/estimated in the sensor or receiver by one of the methods described above) in the receiver.
In a first implementation of the Cone of Possibility Replacement Method, a centerline of the cone can be projected from a number of previous, optionally smoothed, incremental data points (e.g., previous four, 5-minute data points). Each predicted cone centerline point (e.g., 5 minute point) increases by the slope (S) (e.g., for the regression in counts/minute) multiplied by the data point increment (e.g., 5 minutes). Counts/mg/dL is estimated from glucose and sensor range calculation over the data set.
In this first implementation of the Cone of Possibility Replacement Method, positive and negative cone limits are simple linear functions. Periodically (e.g., every 5 minutes), each sensor data point (optionally smoothed) is compared to the cone limits projected from the last four points. If the sensor value observed is within the cone limits, the sensor value is retained and used to generate the cone for the next data point increment (e.g., 5-minute point). If the sensor value observed falls outside the high or low cone limit, the value is replaced by the cone limit value, and that value is used to project the next data point increment (e.g., 5 minute point, high point, or low point). For example, if the difference between two adjacent 5-minute points exceeds 20 mg/dL, then cone limits are capped at 20 mg/dL increments per 5 minutes, with the positive limit of the cone being generated by the addition of 0.5*A*t2 to mid cone value, where A is 0.1 mg/dL/min/min and t is 5 minute increments (A is converted to counts/min/min for the calculation), and the negative limit of the cone being generated by the addition of −0.5*A*t2 to mid cone value. This implementation provides a high degree of accuracy and is minimally sensitive to non-physiological rapid changes.
The following table illustrates one example implementation of the Cone of Possibility Replacement Method, wherein the maximum sustained value observed for S is about +/−4 mg/dL/min and the maximum value observed for A is about +/−0.1 mg/dL/min2:
It is noted that the cone widens for each 5-minute increment for which a sensor value fails to fall inside the cone up to 30 minutes, such as can be seen in the table above. At 30 minutes, a cone has likely widened enough to capture an observed sensor value, which is used, and the cone collapses back to a 5-minute increment width. If no sensor values are captured within 30 minutes, the cone generation routine starts over using the next four observed points. In some implementations special rules can be applied, for example in a case where the change in counts in one 5-minute interval exceeds an estimated 30-mg/dL amount. In this case, the next acceptable point can be more than 20 to 30 minutes later. It is noted that an implementation of this algorithm includes utilizing the cone of possibility to predict glucose levels and alert patients to present or upcoming dangerous blood glucose levels.
In another alternative embodiment of cone widening, the cone can widen in set multiples (e.g., 20 mg/dL) of equivalent amounts for each additional time interval (e.g., 5 minutes), which rapidly widens the cone to accept data.
It is noted that the numerical parameters represent only one example implementation of the Cone of Possibility Replacement Method. The concepts can be applied to any numerical parameters as desired for various glucose sensor applications.
In another implementation of the Cone of Possibility Replacement Method, sensor calibration data is optimized using the Clarke Error Grid, the Consensus Grid, or an alternative error assessment that assigns risk levels based on the accuracy of matched data pairs. In an example using the Clarke Error Grid, because the 10 regions of the Clarke Error Grid are not all symmetric around the Y=X perfect regression, fits to the grid can be improved by using a multi-line regression to the data.
Accordingly the pivot point method for the counts vs. glucose regression fit can be used to optimize sensor calibration data to the Clarke Error Grid, Consensus Grid, or other clinical acceptability standard. First, the glucose range is divided according to meter values (e.g., at 200 mg/dL). Two linear fitting lines are used, which cross at the pivot point. The coordinates of the pivot point in counts and glucose value, plus the slope and intercept of the two lines are variable parameters. Some of pivot point coordinates (e.g., 4 out of 6) and slope or intercept of each line can be reset with each iteration, while the chosen coordinates define the remainder. The data are then re-plotted on the Clarke Error Grid, and changes in point placement and percentages in each region of the grid are evaluated. To optimize the fit of a data set to a Clark Error Grid, the regression of counts vs. reference glucose can be adjusted such that the maximum number of points are in the A+B zones without reducing the A+B percentage, and the number of points are optimized such that the highest percentage are in the A zone and lowest percentage are in the D, E and C zones. In general, the points should be distributed as evenly as possible around the Y=X line. In some embodiments, three distinct lines optimized for clinical acceptability can represent the regression line. In some embodiments, an additional useful criterion can be used to compute the root mean squared percentage bias for the data set. Better fits are characterized by reduction in the total root mean squared percentage bias. In an alternative implementation of the pivot point methods, a predetermined pivot (e.g., 10 degree) of the regression line can be applied when the estimated blood is above or below a set threshold (e.g., 150 mg/dL), wherein the pivot and threshold are determined from a retrospective analysis of the performance of a conversion function and its performance at a range of glucose concentrations.
In another embodiment of Signal Artifacts Replacement, reference changes in electrode potential can be used to estimate glucose sensor data during positive detection of signal artifacts from an electrochemical glucose sensor, the method hereinafter referred to as reference drift replacement. In this embodiment, the electrochemical glucose sensor comprises working, counter, and reference electrodes, such as described with reference to
Such as described with in more detail with reference to
Because of the function of the reference electrode 102, including the drift that occurs during periods of signal artifacts (e.g., ischemia), the reference electrode can be monitored to determine the severity of the signal artifacts on the data stream. Particularly, a substantially direct relationship between the reference electrode drift and signal artifacts has been discovered. Using the information contained within the CV curve (
In alternative implementations of the reference drift replacement method, a variety of algorithms can therefore be implemented that replaces the signal artifacts based on the changes measured in the reference electrode. Linear algorithms, and the like, are suitable for interpreting the direct relationship between reference electrode drift and the non-glucose rate limiting signal noise such that appropriate conversion to signal noise compensation can be derived.
In other embodiments of Signal Artifacts Replacement, prediction algorithms, also referred to as projection algorithms, can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors or the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data. Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation.
In some embodiments, signal replacement/estimation algorithms are used to predict where the glucose signal should be, and if the actual data stream varies beyond a certain threshold of that projected value, then signal artifacts are detected. In alternative embodiments, other data processing can be applied alone, or in combination with the above-described methods, to replace data signals during system noise and/or signal artifacts.
Selective Application of Signal Replacement Algorithms
At block 152, a sensor data receiving module, also referred to as the sensor data module, receives sensor data (e.g., a data stream), including one or more time-spaced sensor data points, such as described in more detail with reference to block 82 in
At block 154, a signal artifacts detection module, also referred to as the signal artifacts detector 154, is programmed to detect transient non-glucose related signal artifacts in the data stream that have a higher amplitude than system noise, such as described in more detail with reference to block 84 in
In one exemplary embodiment, the signal artifacts detection module evaluates the amplitude and/or frequency of the transient non-glucose related signal artifacts, which amplitude and/or frequency can be used to define the severity in terms of a threshold (e.g., high or low) or a numeric representation (e.g., a value from 1 to 10). In another exemplary embodiment, the signal artifacts detection module evaluates a duration of the transient non-glucose related signal artifacts, such that as the duration increases, a severity can be defined in terms of a threshold (e.g., short or long) or a numeric representation (e.g., 10, 20, 30, 40, 50, or 60 minutes). In another exemplary embodiment, the signal artifacts detection module evaluates the frequency content from a Fourier Transform and defines severity in terms of a threshold (e.g., above or below 30 cycles per hour) or a numeric representation (e.g., 50 cycles per hour). All of the signal artifacts detection methods described herein can be implemented to include determining a severity of the signal artifacts, threshold, and/or numerical representations.
At block 156, the signal artifacts replacement module, also referred to as the signal estimation module, selectively applies one of a plurality of signal estimation algorithm factors in response to the severity of said signal artifacts.
In one embodiment, signal artifacts replacement is normally turned off, except during detected signal artifacts. In another embodiment, a first signal estimation algorithm (e.g., linear regression, FIR filter etc.) is turned on all the time, and a second signal estimation algorithm optimized for signal artifacts (e.g., IIR filter, Cone of Possibility Replacement Method, etc.) is turned on only during positive detection of signal artifacts.
In another embodiment, the signal replacement module comprises programming to selectively switch on and off a plurality of distinct signal estimation algorithms based on the severity of the detected signal artifacts. For example, the severity of the signal artifacts can be defined as high and low. In such an example, a first filter (e.g., trimmed regression, linear regression, FIR, Reference Electrode Method, etc.) can be applied during low signal artifacts and a second filter (e.g., IIR, Cone of Possibility Method, etc.) can be applied during high signal artifacts. It is noted that all of the above signal replacement algorithms can be selectively applied in this manner based on the severity of the detected signal artifacts.
In this embodiment, an FIR filter is applied to the data stream during detection of negligible or no signal artifacts (e.g., during no noise to system noise in the data stream). Accordingly, normal signal noise (e.g., system noise) can be filtered to replace the data stream with an FIR-filtered data signal 176, which is illustrated by a slightly heavy solid line. However, upon positive detection of signal artifacts (e.g., detected by approximately zero counter electrode voltage in this embodiment), the FIR filter is switched off and an IIR-filter is switched on in order to replace the signal artifacts with an IIR-filtered glucose signal 178, which is illustrated as a heavy solid line. The IIR filter is subsequently switched off and the FIR filter is switched back on upon detection of negligible signal artifacts (e.g., counter electrode voltage increasing from about zero in this embodiment).
In another embodiment, the signal replacement module comprises programming to selectively apply different parameters to a single signal artifacts replacement algorithm (e.g., IIR, Cone of Possibility Replacement Method, etc.). As an example, the parameters of an algorithm can be switched according to signal artifacts detection; in such an example, an IIR filter with a 30-minute cycle length can be used during times of no noise or system noise and a 60-minute cycle length can be used during signal artifacts. As another example, the severity of the signal artifacts can be defined as short and long; in such an example, an IIR filter with a 30-minute cycle length can be used during the short signal artifacts and a 60-minute cycle length can be used during long signal artifacts. As yet another example, the severity of the signal artifacts can be defined by a numerical representation; in such an example, the numerical representation can be used to calculate the parameters of the signal replacement algorithm (e.g., IIR, Cone of Possibility Replacement Method, and Reference Drift Method).
The above description provides several methods and materials of the invention. This invention is susceptible to modifications in the methods and materials, as well as alterations in the fabrication methods and equipment. Such modifications will become apparent to those skilled in the art from a consideration of this application or practice of the invention provided herein. Consequently, it is not intended that this invention be limited to the specific embodiments provided herein, but that it cover all modifications and alternatives coming within the true scope and spirit of the invention as embodied in the attached claims. All patents, applications, and other references cited herein are hereby incorporated by reference in their entirety.
Any and all priority claims identified in the Application Data Sheet, or any correction thereto, are hereby incorporated by reference under 37 CFR 1.57. This application is a continuation of U.S. application Ser. No. 16/011,414, filed Jun. 18, 2018, which is a continuation of U.S. application Ser. No. 15/894,573, filed Feb. 12, 2018, now abandoned, which is a continuation of U.S. application Ser. No. 15/595,808, filed May 15, 2017, now abandoned, which is a continuation of U.S. application Ser. No. 15/197,349, filed Jun. 29, 2016, now U.S. Pat. No. 9,649,069, which is a continuation of U.S. application Ser. No. 13/181,341, filed Jul. 12, 2011, now U.S. Pat. No. 9,427,183, which is a continuation of U.S. application Ser. No. 10/648,849 filed Aug. 22, 2003, now U.S. Pat. No. 8,010,174. Each of the aforementioned applications is incorporated by reference herein in its entirety, and each is hereby expressly made a part of this specification.
| Number | Name | Date | Kind |
|---|---|---|---|
| 2329161 | Harbert et al. | Sep 1943 | A |
| 2402306 | Turkel | Jun 1946 | A |
| 3210578 | Sherer | Oct 1965 | A |
| 3219533 | Mullins | Nov 1965 | A |
| 3775182 | Patton et al. | Nov 1973 | A |
| 3780727 | King | Dec 1973 | A |
| 3826244 | Salcman et al. | Jul 1974 | A |
| 3898984 | Mandel et al. | Aug 1975 | A |
| 3926760 | Allen et al. | Dec 1975 | A |
| 3929971 | Roy | Dec 1975 | A |
| 3943918 | Lewis | Mar 1976 | A |
| 3957613 | Macur | May 1976 | A |
| 3964974 | Banauch et al. | Jun 1976 | A |
| 3966580 | Janata et al. | Jun 1976 | A |
| 3979274 | Newman | Sep 1976 | A |
| 3982530 | Storch | Sep 1976 | A |
| 4003621 | Lamp | Jan 1977 | A |
| 4024312 | Korpman | May 1977 | A |
| 4036749 | Anderson | Jul 1977 | A |
| 4037563 | Pflueger et al. | Jul 1977 | A |
| 4040908 | Clark, Jr. | Aug 1977 | A |
| 4052754 | Homsy | Oct 1977 | A |
| 4067322 | Johnson | Jan 1978 | A |
| 4073713 | Newman | Feb 1978 | A |
| 4076656 | White et al. | Feb 1978 | A |
| 4172770 | Semersky et al. | Oct 1979 | A |
| 4197840 | Beck et al. | Apr 1980 | A |
| 4215703 | Willson | Aug 1980 | A |
| 4240889 | Yoda et al. | Dec 1980 | A |
| 4253469 | Aslan | Mar 1981 | A |
| 4255500 | Hooke | Mar 1981 | A |
| 4259540 | Sabia | Mar 1981 | A |
| 4260725 | Keogh et al. | Apr 1981 | A |
| 4319578 | Enger | Mar 1982 | A |
| 4327725 | Cortese et al. | May 1982 | A |
| 4353888 | Sefton | Oct 1982 | A |
| 4374013 | Enfors | Feb 1983 | A |
| 4378016 | Loeb | Mar 1983 | A |
| 4388166 | Suzuki et al. | Jun 1983 | A |
| 4402694 | Ash et al. | Sep 1983 | A |
| 4403847 | Chrestensen | Sep 1983 | A |
| 4403984 | Ash et al. | Sep 1983 | A |
| 4415666 | D'Orazio et al. | Nov 1983 | A |
| 4418148 | Oberhardt | Nov 1983 | A |
| 4431004 | Bessman et al. | Feb 1984 | A |
| 4436094 | Cerami | Mar 1984 | A |
| 4442841 | Uehara et al. | Apr 1984 | A |
| 4454295 | Wittmann et al. | Jun 1984 | A |
| 4484987 | Gough | Nov 1984 | A |
| 4494950 | Fischell | Jan 1985 | A |
| 4506680 | Stokes | Mar 1985 | A |
| RE31916 | Oswin et al. | Jun 1985 | E |
| 4534355 | Potter | Aug 1985 | A |
| 4538616 | Rogoff | Sep 1985 | A |
| 4554927 | Fussell | Nov 1985 | A |
| 4577642 | Stokes | Mar 1986 | A |
| 4578215 | Bradley | Mar 1986 | A |
| 4579120 | Macgregor | Apr 1986 | A |
| 4583976 | Ferguson | Apr 1986 | A |
| 4619793 | Lee | Oct 1986 | A |
| RE32361 | Duggan | Feb 1987 | E |
| 4650547 | Gough | Mar 1987 | A |
| 4655880 | Liu | Apr 1987 | A |
| 4663824 | Kenmochi | May 1987 | A |
| 4671288 | Gough | Jun 1987 | A |
| 4672970 | Uchida et al. | Jun 1987 | A |
| 4680268 | Clark, Jr. | Jul 1987 | A |
| 4686044 | Behnke et al. | Aug 1987 | A |
| 4689309 | Jones | Aug 1987 | A |
| 4703756 | Gough et al. | Nov 1987 | A |
| 4711251 | Stokes | Dec 1987 | A |
| 4721677 | Clark, Jr. | Jan 1988 | A |
| 4731726 | Allen, III | Mar 1988 | A |
| 4750496 | Reinhart et al. | Jun 1988 | A |
| 4753652 | Langer et al. | Jun 1988 | A |
| 4757022 | Shults et al. | Jul 1988 | A |
| 4759828 | Young et al. | Jul 1988 | A |
| 4761748 | Le Rat et al. | Aug 1988 | A |
| 4776944 | Janata et al. | Oct 1988 | A |
| 4781798 | Gough | Nov 1988 | A |
| 4787398 | Garcia et al. | Nov 1988 | A |
| 4795435 | Steer | Jan 1989 | A |
| 4803243 | Fujimoto et al. | Feb 1989 | A |
| 4805624 | Yao et al. | Feb 1989 | A |
| 4805625 | Wyler | Feb 1989 | A |
| 4810470 | Burkhardt et al. | Mar 1989 | A |
| 4826706 | Hilker et al. | May 1989 | A |
| 4831070 | McInally et al. | May 1989 | A |
| 4832034 | Pizziconi et al. | May 1989 | A |
| 4849458 | Reed et al. | Jul 1989 | A |
| 4852573 | Kennedy | Aug 1989 | A |
| 4858615 | Meinema | Aug 1989 | A |
| 4861830 | Ward, Jr. | Aug 1989 | A |
| 4863265 | Flower et al. | Sep 1989 | A |
| 4871440 | Nagata et al. | Oct 1989 | A |
| 4883057 | Broderick | Nov 1989 | A |
| 4889744 | Quaid | Dec 1989 | A |
| 4890620 | Gough | Jan 1990 | A |
| 4890621 | Hakky | Jan 1990 | A |
| 4902294 | Gosserez | Feb 1990 | A |
| 4907857 | Giuliani et al. | Mar 1990 | A |
| 4919141 | Zier et al. | Apr 1990 | A |
| 4919414 | Marschke et al. | Apr 1990 | A |
| 4927407 | Dorman | May 1990 | A |
| 4927516 | Yamaguchi et al. | May 1990 | A |
| 4944299 | Silvian | Jul 1990 | A |
| 4952618 | Olsen | Aug 1990 | A |
| 4953552 | Demarzo | Sep 1990 | A |
| 4958148 | Olson | Sep 1990 | A |
| 4960594 | Honeycutt | Oct 1990 | A |
| 4963595 | Ward et al. | Oct 1990 | A |
| 4974592 | Branco | Dec 1990 | A |
| 4974929 | Curry | Dec 1990 | A |
| 4975636 | Desautels | Dec 1990 | A |
| 4984929 | Rock et al. | Jan 1991 | A |
| 4986271 | Wilkins | Jan 1991 | A |
| 4986671 | Sun et al. | Jan 1991 | A |
| 4988341 | Columbus et al. | Jan 1991 | A |
| 4988758 | Fukuda et al. | Jan 1991 | A |
| 4994167 | Shults et al. | Feb 1991 | A |
| 5002572 | Picha | Mar 1991 | A |
| 5007929 | Quaid | Apr 1991 | A |
| 5030333 | Clark, Jr. | Jul 1991 | A |
| 5034112 | Murase et al. | Jul 1991 | A |
| 5050612 | Matsumura | Sep 1991 | A |
| 5055171 | Peck | Oct 1991 | A |
| 5059654 | Hou et al. | Oct 1991 | A |
| 5067491 | Taylor, II et al. | Nov 1991 | A |
| 5068536 | Rosenthal | Nov 1991 | A |
| 5077476 | Rosenthal | Dec 1991 | A |
| 5082550 | Rishpon et al. | Jan 1992 | A |
| 5097834 | Skrabal | Mar 1992 | A |
| 5101814 | Palti | Apr 1992 | A |
| 5108819 | Heller et al. | Apr 1992 | A |
| 5137028 | Nishimura | Aug 1992 | A |
| 5140985 | Schroeder et al. | Aug 1992 | A |
| 5160418 | Mullen | Nov 1992 | A |
| 5165407 | Wilson et al. | Nov 1992 | A |
| 5171689 | Kawaguri et al. | Dec 1992 | A |
| 5190041 | Palti | Mar 1993 | A |
| 5198771 | Fidler et al. | Mar 1993 | A |
| 5208147 | Kagenow et al. | May 1993 | A |
| 5216598 | Branstetter et al. | Jun 1993 | A |
| 5235003 | Ward et al. | Aug 1993 | A |
| 5243516 | White | Sep 1993 | A |
| 5243983 | Tarr et al. | Sep 1993 | A |
| 5249576 | Goldberger et al. | Oct 1993 | A |
| 5264103 | Yoshioka et al. | Nov 1993 | A |
| 5264104 | Gregg et al. | Nov 1993 | A |
| 5266179 | Nankai et al. | Nov 1993 | A |
| 5269891 | Colin | Dec 1993 | A |
| 5271736 | Picha | Dec 1993 | A |
| 5281319 | Kaneko et al. | Jan 1994 | A |
| 5282848 | Schmitt | Feb 1994 | A |
| 5284570 | Savage et al. | Feb 1994 | A |
| 5285513 | Kaufman et al. | Feb 1994 | A |
| 5287753 | Routh et al. | Feb 1994 | A |
| 5299571 | Mastrototaro | Apr 1994 | A |
| 5302440 | Davis | Apr 1994 | A |
| 5304468 | Phillips et al. | Apr 1994 | A |
| 5307263 | Brown | Apr 1994 | A |
| 5310469 | Cunningham et al. | May 1994 | A |
| 5312361 | Zadini et al. | May 1994 | A |
| 5314471 | Brauker et al. | May 1994 | A |
| 5316008 | Suga et al. | May 1994 | A |
| 5322063 | Allen et al. | Jun 1994 | A |
| 5324322 | Grill, Jr. et al. | Jun 1994 | A |
| 5326356 | Della Valle et al. | Jul 1994 | A |
| 5330521 | Cohen | Jul 1994 | A |
| 5330634 | Wong et al. | Jul 1994 | A |
| 5331555 | Hashimoto et al. | Jul 1994 | A |
| 5336102 | Cairns et al. | Aug 1994 | A |
| 5337747 | Neftel | Aug 1994 | A |
| 5342409 | Mullett | Aug 1994 | A |
| 5343869 | Pross et al. | Sep 1994 | A |
| 5344454 | Clarke et al. | Sep 1994 | A |
| 5348788 | White | Sep 1994 | A |
| 5352351 | White et al. | Oct 1994 | A |
| 5354449 | Band et al. | Oct 1994 | A |
| 5356786 | Heller et al. | Oct 1994 | A |
| 5368224 | Richardson et al. | Nov 1994 | A |
| 5372133 | Hogen Esch | Dec 1994 | A |
| 5372719 | Afeyan et al. | Dec 1994 | A |
| 5376070 | Purvis et al. | Dec 1994 | A |
| 5380536 | Hubbell et al. | Jan 1995 | A |
| 5384028 | Ito | Jan 1995 | A |
| 5390671 | Lord et al. | Feb 1995 | A |
| 5391250 | Cheney, II et al. | Feb 1995 | A |
| 5397848 | Yang et al. | Mar 1995 | A |
| 5398682 | Lynn | Mar 1995 | A |
| 5400974 | Musso, Jr. et al. | Mar 1995 | A |
| 5411647 | Johnson et al. | May 1995 | A |
| 5411866 | Luong et al. | May 1995 | A |
| 5425717 | Mohiuddin | Jun 1995 | A |
| 5426032 | Phillips et al. | Jun 1995 | A |
| 5428123 | Ward et al. | Jun 1995 | A |
| 5429735 | Johnson et al. | Jul 1995 | A |
| 5429765 | Flower | Jul 1995 | A |
| 5431160 | Wilkins | Jul 1995 | A |
| 5431921 | Thombre | Jul 1995 | A |
| 5434412 | Sodickson et al. | Jul 1995 | A |
| 5448992 | Kupershmidt | Sep 1995 | A |
| 5453199 | Afeyan et al. | Sep 1995 | A |
| 5453278 | Chan et al. | Sep 1995 | A |
| 5462051 | Oka et al. | Oct 1995 | A |
| 5462064 | D'Angelo et al. | Oct 1995 | A |
| 5469846 | Khan | Nov 1995 | A |
| 5474552 | Palti | Dec 1995 | A |
| 5476094 | Allen et al. | Dec 1995 | A |
| 5482473 | Lord et al. | Jan 1996 | A |
| 5484404 | Schulman et al. | Jan 1996 | A |
| 5491474 | Suni et al. | Feb 1996 | A |
| 5494562 | Maley et al. | Feb 1996 | A |
| 5496453 | Uenoyama et al. | Mar 1996 | A |
| 5497772 | Schulman et al. | Mar 1996 | A |
| 5502396 | Desarzens et al. | Mar 1996 | A |
| 5507288 | Bocker et al. | Apr 1996 | A |
| 5508203 | Fuller et al. | Apr 1996 | A |
| 5513636 | Palti | May 1996 | A |
| 5518601 | Foos et al. | May 1996 | A |
| 5531878 | Vadgama et al. | Jul 1996 | A |
| 5540828 | Yacynych | Jul 1996 | A |
| 5545220 | Andrews et al. | Aug 1996 | A |
| 5545223 | Neuenfeldt et al. | Aug 1996 | A |
| 5549675 | Neuenfeldt et al. | Aug 1996 | A |
| 5553616 | Ham et al. | Sep 1996 | A |
| 5564439 | Picha | Oct 1996 | A |
| 5568806 | Cheney, II et al. | Oct 1996 | A |
| 5569186 | Lord et al. | Oct 1996 | A |
| 5569462 | Martinson et al. | Oct 1996 | A |
| 5571395 | Park et al. | Nov 1996 | A |
| 5575293 | Miller et al. | Nov 1996 | A |
| 5575930 | Tietje-Girault et al. | Nov 1996 | A |
| 5582184 | Erickson et al. | Dec 1996 | A |
| 5584813 | Livingston et al. | Dec 1996 | A |
| 5584876 | Bruchman et al. | Dec 1996 | A |
| 5586553 | Halili et al. | Dec 1996 | A |
| 5587273 | Yan et al. | Dec 1996 | A |
| 5589563 | Ward et al. | Dec 1996 | A |
| 5590651 | Shaffer et al. | Jan 1997 | A |
| 5593440 | Brauker et al. | Jan 1997 | A |
| 5593852 | Heller et al. | Jan 1997 | A |
| 5607565 | Azarnia et al. | Mar 1997 | A |
| 5611900 | Worden et al. | Mar 1997 | A |
| 5620579 | Genshaw et al. | Apr 1997 | A |
| 5624537 | Turner et al. | Apr 1997 | A |
| 5640470 | Iyer et al. | Jun 1997 | A |
| 5653756 | Clarke et al. | Aug 1997 | A |
| 5653863 | Genshaw et al. | Aug 1997 | A |
| 5658330 | Carlisle et al. | Aug 1997 | A |
| 5660163 | Schulman et al. | Aug 1997 | A |
| 5662616 | Bousquet | Sep 1997 | A |
| 5676820 | Wang et al. | Oct 1997 | A |
| 5683562 | Schaffar et al. | Nov 1997 | A |
| 5685644 | Taylor | Nov 1997 | A |
| 5686829 | Girault | Nov 1997 | A |
| 5695623 | Michel et al. | Dec 1997 | A |
| 5696314 | McCaffrey et al. | Dec 1997 | A |
| 5706807 | Picha | Jan 1998 | A |
| 5711302 | Lampropoulos et al. | Jan 1998 | A |
| 5711861 | Ward et al. | Jan 1998 | A |
| 5713888 | Neuenfeldt et al. | Feb 1998 | A |
| 5714123 | Sohrab | Feb 1998 | A |
| 5730654 | Brown | Mar 1998 | A |
| 5733336 | Neuenfeldt et al. | Mar 1998 | A |
| 5741330 | Brauker et al. | Apr 1998 | A |
| 5741634 | Nozoe et al. | Apr 1998 | A |
| 5743262 | Lepper, Jr. et al. | Apr 1998 | A |
| 5749907 | Mann | May 1998 | A |
| 5755226 | Carim et al. | May 1998 | A |
| 5756632 | Ward et al. | May 1998 | A |
| 5766151 | Valley et al. | Jun 1998 | A |
| 5771890 | Tamada | Jun 1998 | A |
| 5776324 | Usala | Jul 1998 | A |
| 5777060 | Van Antwerp | Jul 1998 | A |
| 5779665 | Mastrototaro et al. | Jul 1998 | A |
| 5781455 | Hyodo | Jul 1998 | A |
| 5782912 | Brauker et al. | Jul 1998 | A |
| 5783054 | Raguse et al. | Jul 1998 | A |
| 5787900 | Butler et al. | Aug 1998 | A |
| 5791344 | Schulman et al. | Aug 1998 | A |
| 5795774 | Matsumoto et al. | Aug 1998 | A |
| 5798065 | Picha | Aug 1998 | A |
| 5800420 | Gross et al. | Sep 1998 | A |
| 5800529 | Brauker et al. | Sep 1998 | A |
| 5806517 | Gerhardt et al. | Sep 1998 | A |
| 5807375 | Gross et al. | Sep 1998 | A |
| 5807406 | Brauker et al. | Sep 1998 | A |
| 5811487 | Schulz, Jr. et al. | Sep 1998 | A |
| 5814599 | Mitragotri et al. | Sep 1998 | A |
| 5822715 | Worthington et al. | Oct 1998 | A |
| 5823802 | Bartley | Oct 1998 | A |
| 5826517 | Larson, Jr. et al. | Oct 1998 | A |
| 5833603 | Kovacs et al. | Nov 1998 | A |
| 5836887 | Oka et al. | Nov 1998 | A |
| 5836989 | Shelton | Nov 1998 | A |
| 5837728 | Purcell | Nov 1998 | A |
| 5840240 | Stenoien et al. | Nov 1998 | A |
| 5851197 | Marano et al. | Dec 1998 | A |
| 5861019 | Sun et al. | Jan 1999 | A |
| 5863400 | Drummond et al. | Jan 1999 | A |
| 5871499 | Hahn et al. | Feb 1999 | A |
| 5871514 | Wiklund et al. | Feb 1999 | A |
| 5879373 | Roper et al. | Mar 1999 | A |
| 5882494 | Van Antwerp | Mar 1999 | A |
| 5897578 | Wiklund et al. | Apr 1999 | A |
| 5899855 | Brown | May 1999 | A |
| 5904708 | Goedeke | May 1999 | A |
| 5910554 | Kempe et al. | Jun 1999 | A |
| 5913998 | Butler et al. | Jun 1999 | A |
| 5914026 | Blubaugh, Jr. et al. | Jun 1999 | A |
| 5916445 | Hjerten et al. | Jun 1999 | A |
| 5917346 | Gord | Jun 1999 | A |
| 5919215 | Wiklund et al. | Jul 1999 | A |
| 5928130 | Schmidt | Jul 1999 | A |
| 5928155 | Eggers et al. | Jul 1999 | A |
| 5931814 | Alex et al. | Aug 1999 | A |
| 5933136 | Brown | Aug 1999 | A |
| 5935785 | Reber et al. | Aug 1999 | A |
| 5944661 | Swette et al. | Aug 1999 | A |
| 5951521 | Mastrototaro et al. | Sep 1999 | A |
| 5954643 | Vanantwerp et al. | Sep 1999 | A |
| 5957854 | Besson et al. | Sep 1999 | A |
| 5957903 | Mirzaee et al. | Sep 1999 | A |
| 5959050 | Mosbach et al. | Sep 1999 | A |
| 5961451 | Reber et al. | Oct 1999 | A |
| 5961541 | Ferrati | Oct 1999 | A |
| 5964261 | Neuenfeldt et al. | Oct 1999 | A |
| 5964804 | Brauker et al. | Oct 1999 | A |
| 5964993 | Blubaugh, Jr. et al. | Oct 1999 | A |
| 5965380 | Heller et al. | Oct 1999 | A |
| 5967986 | Cimochowski et al. | Oct 1999 | A |
| 5971922 | Arita et al. | Oct 1999 | A |
| 5972199 | Heller et al. | Oct 1999 | A |
| 5976085 | Kimball et al. | Nov 1999 | A |
| 5985129 | Gough et al. | Nov 1999 | A |
| 5995860 | Sun et al. | Nov 1999 | A |
| 5999848 | Gord et al. | Dec 1999 | A |
| 6001067 | Shults et al. | Dec 1999 | A |
| 6001471 | Bries et al. | Dec 1999 | A |
| 6011984 | Van Antwerp et al. | Jan 2000 | A |
| 6013113 | Mika | Jan 2000 | A |
| 6016448 | Busacker et al. | Jan 2000 | A |
| 6017435 | Hassard et al. | Jan 2000 | A |
| 6023629 | Tamada | Feb 2000 | A |
| 6027445 | Von Bahr | Feb 2000 | A |
| 6036924 | Simons et al. | Mar 2000 | A |
| 6048691 | Maracas | Apr 2000 | A |
| 6049727 | Crothall | Apr 2000 | A |
| 6051372 | Bayerl et al. | Apr 2000 | A |
| 6051389 | Ahl et al. | Apr 2000 | A |
| 6057377 | Sasaki et al. | May 2000 | A |
| 6059946 | Yukawa et al. | May 2000 | A |
| 6063637 | Arnold et al. | May 2000 | A |
| 6065154 | Hulings et al. | May 2000 | A |
| 6081735 | Diab et al. | Jun 2000 | A |
| 6081736 | Colvin et al. | Jun 2000 | A |
| 6083523 | Dionne et al. | Jul 2000 | A |
| 6083710 | Heller et al. | Jul 2000 | A |
| 6088608 | Schulman et al. | Jul 2000 | A |
| 6091975 | Daddona et al. | Jul 2000 | A |
| 6093156 | Cunningham et al. | Jul 2000 | A |
| 6093172 | Funderburk et al. | Jul 2000 | A |
| 6101404 | Yoon et al. | Aug 2000 | A |
| 6103033 | Say et al. | Aug 2000 | A |
| 6103533 | Hassard et al. | Aug 2000 | A |
| 6107083 | Collins et al. | Aug 2000 | A |
| 6115634 | Donders et al. | Sep 2000 | A |
| 6117290 | Say et al. | Sep 2000 | A |
| 6119028 | Schulman et al. | Sep 2000 | A |
| 6120676 | Heller et al. | Sep 2000 | A |
| 6121009 | Heller et al. | Sep 2000 | A |
| 6122536 | Sun et al. | Sep 2000 | A |
| 6123827 | Wong et al. | Sep 2000 | A |
| 6127154 | Mosbach et al. | Oct 2000 | A |
| 6134461 | Say et al. | Oct 2000 | A |
| 6135978 | Houben et al. | Oct 2000 | A |
| 6142939 | Eppstein et al. | Nov 2000 | A |
| 6144869 | Berner et al. | Nov 2000 | A |
| 6144871 | Saito et al. | Nov 2000 | A |
| 6153069 | Pottgen et al. | Nov 2000 | A |
| 6162611 | Heller et al. | Dec 2000 | A |
| 6167614 | Tuttle et al. | Jan 2001 | B1 |
| 6168568 | Gavriely | Jan 2001 | B1 |
| 6169155 | Alvarez et al. | Jan 2001 | B1 |
| 6175752 | Say et al. | Jan 2001 | B1 |
| 6180416 | Kurnik et al. | Jan 2001 | B1 |
| 6187062 | Oweis et al. | Feb 2001 | B1 |
| 6189536 | Martinez et al. | Feb 2001 | B1 |
| 6193873 | Ohara et al. | Feb 2001 | B1 |
| 6200772 | Vadgama et al. | Mar 2001 | B1 |
| 6201980 | Darrow et al. | Mar 2001 | B1 |
| 6201993 | Kruse et al. | Mar 2001 | B1 |
| 6206856 | Mahurkar | Mar 2001 | B1 |
| 6208894 | Schulman et al. | Mar 2001 | B1 |
| 6212416 | Ward et al. | Apr 2001 | B1 |
| 6212417 | Ikeda et al. | Apr 2001 | B1 |
| 6212424 | Robinson | Apr 2001 | B1 |
| 6214185 | Offenbacher et al. | Apr 2001 | B1 |
| 6219574 | Cormier et al. | Apr 2001 | B1 |
| 6223080 | Thompson | Apr 2001 | B1 |
| 6223083 | Rosar | Apr 2001 | B1 |
| 6230059 | Duffin | May 2001 | B1 |
| 6231879 | Li et al. | May 2001 | B1 |
| 6232783 | Merrill | May 2001 | B1 |
| 6233080 | Brenner et al. | May 2001 | B1 |
| 6233471 | Berner et al. | May 2001 | B1 |
| 6241862 | McAleer et al. | Jun 2001 | B1 |
| 6241863 | Monbouquette | Jun 2001 | B1 |
| 6248067 | Causey, III et al. | Jun 2001 | B1 |
| 6251280 | Dai et al. | Jun 2001 | B1 |
| 6255592 | Pennington et al. | Jul 2001 | B1 |
| 6256522 | Schultz | Jul 2001 | B1 |
| 6259937 | Schulman et al. | Jul 2001 | B1 |
| 6264825 | Blackburn et al. | Jul 2001 | B1 |
| 6268161 | Han et al. | Jul 2001 | B1 |
| 6272364 | Kurnik | Aug 2001 | B1 |
| 6272480 | Tresp et al. | Aug 2001 | B1 |
| 6274285 | Gries et al. | Aug 2001 | B1 |
| 6274686 | Mosbach et al. | Aug 2001 | B1 |
| 6275717 | Gross et al. | Aug 2001 | B1 |
| 6284126 | Kurnik et al. | Sep 2001 | B1 |
| 6284478 | Heller et al. | Sep 2001 | B1 |
| 6285897 | Kilcoyne et al. | Sep 2001 | B1 |
| 6293925 | Safabash et al. | Sep 2001 | B1 |
| 6296615 | Brockway et al. | Oct 2001 | B1 |
| 6298254 | Tamada | Oct 2001 | B2 |
| 6299578 | Kurnik et al. | Oct 2001 | B1 |
| 6300002 | Webb et al. | Oct 2001 | B1 |
| 6302855 | Lav et al. | Oct 2001 | B1 |
| 6309351 | Kurnik et al. | Oct 2001 | B1 |
| 6309384 | Harrington et al. | Oct 2001 | B1 |
| 6309884 | Cooper et al. | Oct 2001 | B1 |
| 6310110 | Markowitz et al. | Oct 2001 | B1 |
| 6315738 | Nishikawa et al. | Nov 2001 | B1 |
| 6319566 | Polanyi et al. | Nov 2001 | B1 |
| 6325978 | Labuda et al. | Dec 2001 | B1 |
| 6325979 | Hahn et al. | Dec 2001 | B1 |
| 6326160 | Dunn et al. | Dec 2001 | B1 |
| 6329161 | Heller et al. | Dec 2001 | B1 |
| 6329929 | Weijand et al. | Dec 2001 | B1 |
| 6330464 | Colvin, Jr. et al. | Dec 2001 | B1 |
| 6343225 | Clark, Jr. | Jan 2002 | B1 |
| 6356776 | Berner et al. | Mar 2002 | B1 |
| 6356786 | Rezai et al. | Mar 2002 | B1 |
| 6365670 | Fry | Apr 2002 | B1 |
| 6366794 | Moussy et al. | Apr 2002 | B1 |
| 6368141 | Vanantwerp et al. | Apr 2002 | B1 |
| 6368274 | Van Antwerp et al. | Apr 2002 | B1 |
| 6370941 | Nakamura et al. | Apr 2002 | B2 |
| 6372244 | Antanavich et al. | Apr 2002 | B1 |
| 6379301 | Worthington et al. | Apr 2002 | B1 |
| 6379317 | Kintzig et al. | Apr 2002 | B1 |
| 6387709 | Mason et al. | May 2002 | B1 |
| 6400974 | Lesho | Jun 2002 | B1 |
| 6405066 | Essenpreis et al. | Jun 2002 | B1 |
| 6406066 | Uegane | Jun 2002 | B1 |
| 6406426 | Reuss et al. | Jun 2002 | B1 |
| 6409674 | Brockway et al. | Jun 2002 | B1 |
| 6413393 | Van Antwerp et al. | Jul 2002 | B1 |
| 6416651 | Millar | Jul 2002 | B1 |
| 6418332 | Mastrototaro et al. | Jul 2002 | B1 |
| 6424847 | Mastrototaro et al. | Jul 2002 | B1 |
| 6433602 | Lall et al. | Aug 2002 | B1 |
| 6447448 | Ishikawa et al. | Sep 2002 | B1 |
| 6447542 | Weadock | Sep 2002 | B1 |
| 6454710 | Ballerstadt et al. | Sep 2002 | B1 |
| 6461496 | Feldman et al. | Oct 2002 | B1 |
| 6466810 | Ward et al. | Oct 2002 | B1 |
| 6471689 | Joseph et al. | Oct 2002 | B1 |
| 6475372 | Ohara et al. | Nov 2002 | B1 |
| 6475750 | Han et al. | Nov 2002 | B1 |
| 6477392 | Honigs et al. | Nov 2002 | B1 |
| 6477395 | Schulman et al. | Nov 2002 | B2 |
| 6481440 | Gielen et al. | Nov 2002 | B2 |
| 6484046 | Say et al. | Nov 2002 | B1 |
| 6484132 | Hively et al. | Nov 2002 | B1 |
| 6487429 | Hockersmith et al. | Nov 2002 | B2 |
| 6494830 | Wessel | Dec 2002 | B1 |
| 6498043 | Schulman et al. | Dec 2002 | B1 |
| 6501976 | Sohrab | Dec 2002 | B1 |
| 6510329 | Heckel | Jan 2003 | B2 |
| 6512939 | Colvin et al. | Jan 2003 | B1 |
| 6514718 | Heller et al. | Feb 2003 | B2 |
| 6520326 | McIvor et al. | Feb 2003 | B2 |
| 6520997 | Pekkarinen et al. | Feb 2003 | B1 |
| 6526298 | Khalil et al. | Feb 2003 | B1 |
| 6527729 | Turcott | Mar 2003 | B1 |
| 6537318 | Ita et al. | Mar 2003 | B1 |
| 6541107 | Zhong et al. | Apr 2003 | B1 |
| 6541266 | Modzelewski et al. | Apr 2003 | B2 |
| 6544212 | Galley et al. | Apr 2003 | B2 |
| 6545085 | Kilgour et al. | Apr 2003 | B2 |
| 6546268 | Ishikawa et al. | Apr 2003 | B1 |
| 6546269 | Kurnik | Apr 2003 | B1 |
| 6547839 | Zhang et al. | Apr 2003 | B2 |
| 6551496 | Moles et al. | Apr 2003 | B1 |
| 6553241 | Mannheimer et al. | Apr 2003 | B2 |
| 6553244 | Lesho et al. | Apr 2003 | B2 |
| 6558320 | Causey, III et al. | May 2003 | B1 |
| 6558321 | Burd et al. | May 2003 | B1 |
| 6558351 | Steil et al. | May 2003 | B1 |
| 6560471 | Heller et al. | May 2003 | B1 |
| 6561978 | Conn et al. | May 2003 | B1 |
| 6565509 | Say et al. | May 2003 | B1 |
| 6569521 | Sheridan et al. | May 2003 | B1 |
| 6572545 | Knobbe et al. | Jun 2003 | B2 |
| 6574490 | Abbink et al. | Jun 2003 | B2 |
| 6575905 | Knobbe et al. | Jun 2003 | B2 |
| 6579498 | Eglise | Jun 2003 | B1 |
| 6579690 | Bonnecaze et al. | Jun 2003 | B1 |
| 6584335 | Haar et al. | Jun 2003 | B1 |
| 6585644 | Lebel et al. | Jul 2003 | B2 |
| 6585763 | Keilman et al. | Jul 2003 | B1 |
| 6589229 | Connelly et al. | Jul 2003 | B1 |
| 6591125 | Buse et al. | Jul 2003 | B1 |
| 6595919 | Berner et al. | Jul 2003 | B2 |
| 6603987 | Whitson | Aug 2003 | B2 |
| 6605072 | Struys et al. | Aug 2003 | B2 |
| 6607509 | Bobroff et al. | Aug 2003 | B2 |
| 6612984 | Kerr, II | Sep 2003 | B1 |
| 6613379 | Ward et al. | Sep 2003 | B2 |
| 6615078 | Burson et al. | Sep 2003 | B1 |
| 6618934 | Feldman et al. | Sep 2003 | B1 |
| 6623619 | Saffell et al. | Sep 2003 | B2 |
| 6633772 | Ford et al. | Oct 2003 | B2 |
| 6641533 | Causey, III et al. | Nov 2003 | B2 |
| 6642015 | Vachon et al. | Nov 2003 | B2 |
| 6645181 | Lavi et al. | Nov 2003 | B1 |
| 6648821 | Lebel et al. | Nov 2003 | B2 |
| 6653091 | Dunn et al. | Nov 2003 | B1 |
| 6654625 | Say et al. | Nov 2003 | B1 |
| 6673022 | Bobo et al. | Jan 2004 | B1 |
| 6673596 | Sayler et al. | Jan 2004 | B1 |
| 6683535 | Utke | Jan 2004 | B1 |
| 6687522 | Tamada | Feb 2004 | B2 |
| 6694191 | Starkweather et al. | Feb 2004 | B2 |
| 6695860 | Ward et al. | Feb 2004 | B1 |
| 6699188 | Wessel | Mar 2004 | B2 |
| 6699218 | Flaherty et al. | Mar 2004 | B2 |
| 6702857 | Brauker et al. | Mar 2004 | B2 |
| 6702972 | Markle | Mar 2004 | B1 |
| 6705833 | Tam et al. | Mar 2004 | B2 |
| 6721587 | Gough | Apr 2004 | B2 |
| 6731976 | Penn et al. | May 2004 | B2 |
| 6740059 | Flaherty | May 2004 | B2 |
| 6740075 | Lebel et al. | May 2004 | B2 |
| 6741877 | Shults et al. | May 2004 | B1 |
| 6743635 | Neel et al. | Jun 2004 | B2 |
| 6809653 | Mann et al. | Oct 2004 | B1 |
| 6810290 | Lebel et al. | Oct 2004 | B2 |
| 6815186 | Clark, Jr. | Nov 2004 | B2 |
| 6837988 | Leong et al. | Jan 2005 | B2 |
| 6862465 | Shults et al. | Mar 2005 | B2 |
| 6869413 | Langley et al. | Mar 2005 | B2 |
| 6872298 | Kermani | Mar 2005 | B2 |
| 6875386 | Ward et al. | Apr 2005 | B1 |
| 6890421 | Ohara et al. | May 2005 | B2 |
| 6891317 | Pei et al. | May 2005 | B2 |
| 6892085 | McIvor et al. | May 2005 | B2 |
| 6893552 | Wang et al. | May 2005 | B1 |
| 6895263 | Shin et al. | May 2005 | B2 |
| 6925393 | Kalatz et al. | Aug 2005 | B1 |
| 6931327 | Goode, Jr. et al. | Aug 2005 | B2 |
| 6936006 | Sabra | Aug 2005 | B2 |
| 6952604 | Denuzzio et al. | Oct 2005 | B2 |
| 6965791 | Hitchcock et al. | Nov 2005 | B1 |
| 6998247 | Monfre et al. | Feb 2006 | B2 |
| 7003336 | Holker et al. | Feb 2006 | B2 |
| 7003341 | Say et al. | Feb 2006 | B2 |
| 7006856 | Baker, Jr. et al. | Feb 2006 | B2 |
| 7011630 | Desai et al. | Mar 2006 | B2 |
| 7016713 | Gardner et al. | Mar 2006 | B2 |
| 7022072 | Fox et al. | Apr 2006 | B2 |
| 7025743 | Mann et al. | Apr 2006 | B2 |
| 7027848 | Robinson et al. | Apr 2006 | B2 |
| 7029444 | Shin et al. | Apr 2006 | B2 |
| 7060059 | Keith et al. | Jun 2006 | B2 |
| 7070580 | Nielsen | Jul 2006 | B2 |
| 7074307 | Simpson et al. | Jul 2006 | B2 |
| 7078582 | Stebbings et al. | Jul 2006 | B2 |
| 7090764 | Iyengar et al. | Aug 2006 | B2 |
| 7098803 | Mann et al. | Aug 2006 | B2 |
| 7108778 | Simpson et al. | Sep 2006 | B2 |
| 7132041 | Deng et al. | Nov 2006 | B2 |
| 7134999 | Brauker et al. | Nov 2006 | B2 |
| 7153265 | Vachon | Dec 2006 | B2 |
| 7169289 | Schulein et al. | Jan 2007 | B2 |
| 7183102 | Monfre et al. | Feb 2007 | B2 |
| 7190988 | Say et al. | Mar 2007 | B2 |
| 7192450 | Brauker et al. | Mar 2007 | B2 |
| 7207974 | Safabash et al. | Apr 2007 | B2 |
| 7229288 | Stuart et al. | Jun 2007 | B2 |
| 7241265 | Cummings et al. | Jul 2007 | B2 |
| 7260393 | Fnu et al. | Aug 2007 | B2 |
| 7261690 | Teller et al. | Aug 2007 | B2 |
| 7267665 | Steil et al. | Sep 2007 | B2 |
| 7276029 | Goode, Jr. et al. | Oct 2007 | B2 |
| 7278983 | Ireland et al. | Oct 2007 | B2 |
| 7295867 | Berner et al. | Nov 2007 | B2 |
| 7299082 | Feldman et al. | Nov 2007 | B2 |
| 7344499 | Prausnitz et al. | Mar 2008 | B1 |
| 7344626 | Harding et al. | Mar 2008 | B2 |
| 7354420 | Steil et al. | Apr 2008 | B2 |
| 7359723 | Jones | Apr 2008 | B2 |
| 7372277 | Diamond et al. | May 2008 | B2 |
| 7381184 | Funderburk et al. | Jun 2008 | B2 |
| 7399277 | Saidara et al. | Jul 2008 | B2 |
| 7399585 | Gau | Jul 2008 | B2 |
| 7402153 | Steil et al. | Jul 2008 | B2 |
| 7417164 | Suri | Aug 2008 | B2 |
| 7422727 | Lee et al. | Sep 2008 | B2 |
| 7426408 | Denuzzio et al. | Sep 2008 | B2 |
| 7433727 | Ward et al. | Oct 2008 | B2 |
| 7501052 | Iyengar et al. | Mar 2009 | B2 |
| 7517439 | Harding et al. | Apr 2009 | B2 |
| 7519408 | Rasdal et al. | Apr 2009 | B2 |
| 7519478 | Bartkowiak et al. | Apr 2009 | B2 |
| 7523004 | Bartkowiak et al. | Apr 2009 | B2 |
| 7547382 | Harding et al. | Jun 2009 | B2 |
| 7583990 | Goode, Jr. et al. | Sep 2009 | B2 |
| 7587287 | Connolly et al. | Sep 2009 | B2 |
| 7591801 | Brauker et al. | Sep 2009 | B2 |
| 7599726 | Goode, Jr. et al. | Oct 2009 | B2 |
| 7604593 | Parris et al. | Oct 2009 | B2 |
| 7618368 | Brown | Nov 2009 | B2 |
| 7618369 | Hayter et al. | Nov 2009 | B2 |
| 7624028 | Brown | Nov 2009 | B1 |
| 7636602 | Baru Fassio et al. | Dec 2009 | B2 |
| 7640032 | Jones | Dec 2009 | B2 |
| 7640048 | Dobbles et al. | Dec 2009 | B2 |
| 7645374 | Diamond et al. | Jan 2010 | B2 |
| 7647237 | Malave et al. | Jan 2010 | B2 |
| 7664089 | Zhao | Feb 2010 | B2 |
| 7695434 | Malecha | Apr 2010 | B2 |
| 7711402 | Shults et al. | May 2010 | B2 |
| 7713392 | Harding et al. | May 2010 | B2 |
| 7731659 | Malecha | Jun 2010 | B2 |
| 7761126 | Gardner et al. | Jul 2010 | B2 |
| 7766830 | Fox et al. | Aug 2010 | B2 |
| 7771352 | Shults et al. | Aug 2010 | B2 |
| 7774145 | Brauker et al. | Aug 2010 | B2 |
| 7792562 | Shults et al. | Sep 2010 | B2 |
| 7905833 | Brister et al. | Mar 2011 | B2 |
| 7935057 | Goode, Jr. et al. | May 2011 | B2 |
| 7943034 | Diamond et al. | May 2011 | B2 |
| 7946984 | Brister et al. | May 2011 | B2 |
| 7949381 | Brister et al. | May 2011 | B2 |
| 7964089 | Harding et al. | Jun 2011 | B2 |
| 7964146 | Harding et al. | Jun 2011 | B2 |
| 7998071 | Goode, Jr. et al. | Aug 2011 | B2 |
| 8005524 | Brauker et al. | Aug 2011 | B2 |
| 8005525 | Goode, Jr. et al. | Aug 2011 | B2 |
| 8010174 | Goode, Jr. et al. | Aug 2011 | B2 |
| 8016997 | Diamond et al. | Sep 2011 | B2 |
| 8030942 | Diamond et al. | Oct 2011 | B2 |
| 8060173 | Goode, Jr. et al. | Nov 2011 | B2 |
| 8073519 | Goode, Jr. et al. | Dec 2011 | B2 |
| 8073520 | Kamath et al. | Dec 2011 | B2 |
| 8123920 | Iyengar et al. | Feb 2012 | B2 |
| 8128562 | Goode, Jr. | Mar 2012 | B2 |
| 8150488 | Goode, Jr. et al. | Apr 2012 | B2 |
| 8165651 | Feldman et al. | Apr 2012 | B2 |
| 8167801 | Goode, Jr. et al. | May 2012 | B2 |
| 8195265 | Goode, Jr. et al. | Jun 2012 | B2 |
| 8229536 | Goode, Jr. et al. | Jul 2012 | B2 |
| 8233959 | Kamath et al. | Jul 2012 | B2 |
| 8238562 | Allamanche et al. | Aug 2012 | B2 |
| 8260393 | Kamath et al. | Sep 2012 | B2 |
| 8282549 | Brauker et al. | Oct 2012 | B2 |
| 8292810 | Goode, Jr. et al. | Oct 2012 | B2 |
| 8346338 | Goode, Jr. et al. | Jan 2013 | B2 |
| 8412301 | Goode, Jr. et al. | Apr 2013 | B2 |
| 8423113 | Shariati et al. | Apr 2013 | B2 |
| 8435179 | Goode, Jr. et al. | May 2013 | B2 |
| 8491474 | Goode, Jr. et al. | Jul 2013 | B2 |
| 8657747 | Kamath et al. | Feb 2014 | B2 |
| 8672845 | Kamath et al. | Mar 2014 | B2 |
| 8777853 | Kamath et al. | Jul 2014 | B2 |
| 8790260 | Goode, Jr. et al. | Jul 2014 | B2 |
| 8795177 | Goode, Jr. et al. | Aug 2014 | B2 |
| 8812073 | Goode, Jr. et al. | Aug 2014 | B2 |
| 8821400 | Goode, Jr. et al. | Sep 2014 | B2 |
| 8843187 | Goode, Jr. et al. | Sep 2014 | B2 |
| 9149219 | Goode, Jr. et al. | Oct 2015 | B2 |
| 9247901 | Kamath et al. | Feb 2016 | B2 |
| 9282925 | Kamath et al. | Mar 2016 | B2 |
| 9420968 | Kamath et al. | Aug 2016 | B2 |
| 9427183 | Goode, V et al. | Aug 2016 | B2 |
| 9510782 | Kamath et al. | Dec 2016 | B2 |
| 9585607 | Kamath et al. | Mar 2017 | B2 |
| 9649069 | Goode et al. | May 2017 | B2 |
| 9724045 | Goode, V et al. | Aug 2017 | B1 |
| 9750460 | Goode et al. | Sep 2017 | B2 |
| 20010016682 | Berner et al. | Aug 2001 | A1 |
| 20010019022 | Nakao et al. | Sep 2001 | A1 |
| 20010041830 | Varalli et al. | Nov 2001 | A1 |
| 20010051768 | Schulman et al. | Dec 2001 | A1 |
| 20020016535 | Martin et al. | Feb 2002 | A1 |
| 20020019022 | Dunn et al. | Feb 2002 | A1 |
| 20020022883 | Burg | Feb 2002 | A1 |
| 20020023852 | Mcivor et al. | Feb 2002 | A1 |
| 20020026110 | Parris et al. | Feb 2002 | A1 |
| 20020026111 | Ackerman | Feb 2002 | A1 |
| 20020042090 | Heller et al. | Apr 2002 | A1 |
| 20020042561 | Schulman et al. | Apr 2002 | A1 |
| 20020045808 | Ford et al. | Apr 2002 | A1 |
| 20020055673 | Van Antwerp et al. | May 2002 | A1 |
| 20020065453 | Lesho et al. | May 2002 | A1 |
| 20020068860 | Clark, Jr. | Jun 2002 | A1 |
| 20020084196 | Liamos et al. | Jul 2002 | A1 |
| 20020099282 | Knobbe et al. | Jul 2002 | A1 |
| 20020099997 | Piret | Jul 2002 | A1 |
| 20020111547 | Knobbe et al. | Aug 2002 | A1 |
| 20020115920 | Rich et al. | Aug 2002 | A1 |
| 20020119711 | Vanantwerp et al. | Aug 2002 | A1 |
| 20020123048 | Gau | Sep 2002 | A1 |
| 20020128546 | Silver | Sep 2002 | A1 |
| 20020137991 | Scarantino et al. | Sep 2002 | A1 |
| 20020139489 | Grogg | Oct 2002 | A1 |
| 20020151796 | Koulik | Oct 2002 | A1 |
| 20020151816 | Rich et al. | Oct 2002 | A1 |
| 20020155615 | Novikov et al. | Oct 2002 | A1 |
| 20020161288 | Shin | Oct 2002 | A1 |
| 20020169369 | Ward et al. | Nov 2002 | A1 |
| 20020177763 | Burns et al. | Nov 2002 | A1 |
| 20020177764 | Sohrab | Nov 2002 | A1 |
| 20020182241 | Borenstein et al. | Dec 2002 | A1 |
| 20020185384 | Leong et al. | Dec 2002 | A1 |
| 20020188185 | Sohrab | Dec 2002 | A1 |
| 20020188216 | Kayyali et al. | Dec 2002 | A1 |
| 20020193885 | Legeay et al. | Dec 2002 | A1 |
| 20020198513 | Lebel et al. | Dec 2002 | A1 |
| 20030003524 | Taniike et al. | Jan 2003 | A1 |
| 20030004432 | Assenheimer | Jan 2003 | A1 |
| 20030004457 | Andersson | Jan 2003 | A1 |
| 20030006669 | Pei et al. | Jan 2003 | A1 |
| 20030023171 | Sato et al. | Jan 2003 | A1 |
| 20030023317 | Brauker et al. | Jan 2003 | A1 |
| 20030028089 | Galley et al. | Feb 2003 | A1 |
| 20030032874 | Rhodes et al. | Feb 2003 | A1 |
| 20030050537 | Wessel | Mar 2003 | A1 |
| 20030050546 | Desai et al. | Mar 2003 | A1 |
| 20030054428 | Monfre et al. | Mar 2003 | A1 |
| 20030060765 | Campbell et al. | Mar 2003 | A1 |
| 20030070548 | Clausen | Apr 2003 | A1 |
| 20030076082 | Morgan et al. | Apr 2003 | A1 |
| 20030078481 | McIvor et al. | Apr 2003 | A1 |
| 20030078560 | Miller et al. | Apr 2003 | A1 |
| 20030097082 | Purdy et al. | May 2003 | A1 |
| 20030100040 | Bonnecaze et al. | May 2003 | A1 |
| 20030100821 | Heller et al. | May 2003 | A1 |
| 20030117296 | Seely | Jun 2003 | A1 |
| 20030119208 | Yoon et al. | Jun 2003 | A1 |
| 20030120152 | Omiya | Jun 2003 | A1 |
| 20030121346 | Watanabe et al. | Jul 2003 | A1 |
| 20030125612 | Fox et al. | Jul 2003 | A1 |
| 20030125613 | Enegren et al. | Jul 2003 | A1 |
| 20030130616 | Steil et al. | Jul 2003 | A1 |
| 20030132227 | Geisler et al. | Jul 2003 | A1 |
| 20030134347 | Heller et al. | Jul 2003 | A1 |
| 20030176183 | Drucker et al. | Sep 2003 | A1 |
| 20030181794 | Rini et al. | Sep 2003 | A1 |
| 20030187338 | Say et al. | Oct 2003 | A1 |
| 20030188427 | Say et al. | Oct 2003 | A1 |
| 20030199744 | Buse et al. | Oct 2003 | A1 |
| 20030199745 | Burson et al. | Oct 2003 | A1 |
| 20030199878 | Pohjonen et al. | Oct 2003 | A1 |
| 20030203498 | Neel et al. | Oct 2003 | A1 |
| 20030208113 | Mault et al. | Nov 2003 | A1 |
| 20030211625 | Cohan et al. | Nov 2003 | A1 |
| 20030212317 | Kovatchev et al. | Nov 2003 | A1 |
| 20030212346 | Yuzhakov et al. | Nov 2003 | A1 |
| 20030212347 | Sohrab | Nov 2003 | A1 |
| 20030217966 | Tapsak et al. | Nov 2003 | A1 |
| 20030225361 | Sabra | Dec 2003 | A1 |
| 20030225437 | Ferguson | Dec 2003 | A1 |
| 20030235817 | Bartkowiak et al. | Dec 2003 | A1 |
| 20040010207 | Flaherty et al. | Jan 2004 | A1 |
| 20040011671 | Shults et al. | Jan 2004 | A1 |
| 20040015063 | Denuzzio et al. | Jan 2004 | A1 |
| 20040015134 | Lavi et al. | Jan 2004 | A1 |
| 20040024327 | Brodnick | Feb 2004 | A1 |
| 20040030285 | Lavi et al. | Feb 2004 | A1 |
| 20040030294 | Mahurkar | Feb 2004 | A1 |
| 20040039298 | Abreu | Feb 2004 | A1 |
| 20040039406 | Jessen | Feb 2004 | A1 |
| 20040045879 | Shults et al. | Mar 2004 | A1 |
| 20040052689 | Yao | Mar 2004 | A1 |
| 20040064156 | Shah et al. | Apr 2004 | A1 |
| 20040068230 | Estes et al. | Apr 2004 | A1 |
| 20040074785 | Holker et al. | Apr 2004 | A1 |
| 20040078219 | Kaylor et al. | Apr 2004 | A1 |
| 20040087671 | Tamada et al. | May 2004 | A1 |
| 20040106857 | Gough | Jun 2004 | A1 |
| 20040106859 | Say et al. | Jun 2004 | A1 |
| 20040120848 | Teodorczyk | Jun 2004 | A1 |
| 20040133164 | Funderburk et al. | Jul 2004 | A1 |
| 20040138540 | Baker, Jr. et al. | Jul 2004 | A1 |
| 20040143173 | Reghabi et al. | Jul 2004 | A1 |
| 20040146909 | Duong et al. | Jul 2004 | A1 |
| 20040152187 | Haight et al. | Aug 2004 | A1 |
| 20040152622 | Keith et al. | Aug 2004 | A1 |
| 20040167801 | Say et al. | Aug 2004 | A1 |
| 20040173472 | Jung et al. | Sep 2004 | A1 |
| 20040176672 | Silver et al. | Sep 2004 | A1 |
| 20040186362 | Brauker et al. | Sep 2004 | A1 |
| 20040186365 | Jin et al. | Sep 2004 | A1 |
| 20040196362 | Hoshino et al. | Oct 2004 | A1 |
| 20040199059 | Brauker et al. | Oct 2004 | A1 |
| 20040219664 | Heller et al. | Nov 2004 | A1 |
| 20040220517 | Starkweather et al. | Nov 2004 | A1 |
| 20040236200 | Say et al. | Nov 2004 | A1 |
| 20040236251 | Roe et al. | Nov 2004 | A1 |
| 20040242982 | Sakata et al. | Dec 2004 | A1 |
| 20040254433 | Bandis et al. | Dec 2004 | A1 |
| 20050004439 | Shin et al. | Jan 2005 | A1 |
| 20050006122 | Burnette | Jan 2005 | A1 |
| 20050010265 | Baru Fassio et al. | Jan 2005 | A1 |
| 20050026689 | Marks | Feb 2005 | A1 |
| 20050027180 | Goode, Jr. et al. | Feb 2005 | A1 |
| 20050027181 | Goode, Jr. et al. | Feb 2005 | A1 |
| 20050027182 | Siddiqui et al. | Feb 2005 | A1 |
| 20050027462 | Goode et al. | Feb 2005 | A1 |
| 20050027463 | Goode, Jr. et al. | Feb 2005 | A1 |
| 20050031689 | Shults et al. | Feb 2005 | A1 |
| 20050033132 | Shults et al. | Feb 2005 | A1 |
| 20050038332 | Saidara et al. | Feb 2005 | A1 |
| 20050043598 | Goode, Jr. et al. | Feb 2005 | A1 |
| 20050051427 | Brauker et al. | Mar 2005 | A1 |
| 20050051440 | Simpson et al. | Mar 2005 | A1 |
| 20050054909 | Petisce et al. | Mar 2005 | A1 |
| 20050056552 | Simpson et al. | Mar 2005 | A1 |
| 20050056652 | Cezeaux | Mar 2005 | A1 |
| 20050090607 | Tapsak et al. | Apr 2005 | A1 |
| 20050096519 | Denuzzio et al. | May 2005 | A1 |
| 20050101847 | Routt et al. | May 2005 | A1 |
| 20050112169 | Brauker et al. | May 2005 | A1 |
| 20050113653 | Fox et al. | May 2005 | A1 |
| 20050115832 | Simpson et al. | Jun 2005 | A1 |
| 20050121322 | Say et al. | Jun 2005 | A1 |
| 20050139489 | Davies et al. | Jun 2005 | A1 |
| 20050143635 | Kamath et al. | Jun 2005 | A1 |
| 20050143675 | Neel et al. | Jun 2005 | A1 |
| 20050154271 | Rasdal et al. | Jul 2005 | A1 |
| 20050173245 | Feldman et al. | Aug 2005 | A1 |
| 20050187720 | Goode, Jr. et al. | Aug 2005 | A1 |
| 20050192557 | Brauker et al. | Sep 2005 | A1 |
| 20050203360 | Brauker et al. | Sep 2005 | A1 |
| 20050203364 | Monfre et al. | Sep 2005 | A1 |
| 20050211571 | Schulein et al. | Sep 2005 | A1 |
| 20050215871 | Feldman et al. | Sep 2005 | A1 |
| 20050215872 | Berner et al. | Sep 2005 | A1 |
| 20050239154 | Feldman et al. | Oct 2005 | A1 |
| 20050242479 | Petisce et al. | Nov 2005 | A1 |
| 20050245795 | Goode, Jr. et al. | Nov 2005 | A1 |
| 20050245799 | Brauker et al. | Nov 2005 | A1 |
| 20050261563 | Zhou et al. | Nov 2005 | A1 |
| 20060008370 | Massaro et al. | Jan 2006 | A1 |
| 20060015020 | Neale et al. | Jan 2006 | A1 |
| 20060015024 | Brister et al. | Jan 2006 | A1 |
| 20060016700 | Brister et al. | Jan 2006 | A1 |
| 20060019327 | Brister et al. | Jan 2006 | A1 |
| 20060020186 | Brister et al. | Jan 2006 | A1 |
| 20060020187 | Brister et al. | Jan 2006 | A1 |
| 20060020188 | Kamath et al. | Jan 2006 | A1 |
| 20060020189 | Brister et al. | Jan 2006 | A1 |
| 20060020190 | Kamath et al. | Jan 2006 | A1 |
| 20060020191 | Brister et al. | Jan 2006 | A1 |
| 20060020192 | Brister et al. | Jan 2006 | A1 |
| 20060036139 | Brister et al. | Feb 2006 | A1 |
| 20060036140 | Brister et al. | Feb 2006 | A1 |
| 20060036141 | Kamath et al. | Feb 2006 | A1 |
| 20060036142 | Brister et al. | Feb 2006 | A1 |
| 20060036143 | Brister et al. | Feb 2006 | A1 |
| 20060036144 | Brister et al. | Feb 2006 | A1 |
| 20060036145 | Brister et al. | Feb 2006 | A1 |
| 20060040402 | Brauker et al. | Feb 2006 | A1 |
| 20060047192 | Hellwig et al. | Mar 2006 | A1 |
| 20060047215 | Newman et al. | Mar 2006 | A1 |
| 20060100588 | Brunnberg et al. | May 2006 | A1 |
| 20060183984 | Dobbles et al. | Aug 2006 | A1 |
| 20060183985 | Brister et al. | Aug 2006 | A1 |
| 20060200009 | Wekell et al. | Sep 2006 | A1 |
| 20060222566 | Brauker et al. | Oct 2006 | A1 |
| 20060258929 | Goode, Jr. et al. | Nov 2006 | A1 |
| 20060281985 | Ward et al. | Dec 2006 | A1 |
| 20070016381 | Kamath et al. | Jan 2007 | A1 |
| 20070032706 | Kamath et al. | Feb 2007 | A1 |
| 20070049873 | Hansen et al. | Mar 2007 | A1 |
| 20070066873 | Kamath et al. | Mar 2007 | A1 |
| 20070073129 | Shah et al. | Mar 2007 | A1 |
| 20070203410 | Say et al. | Aug 2007 | A1 |
| 20070203966 | Brauker et al. | Aug 2007 | A1 |
| 20070208244 | Brauker et al. | Sep 2007 | A1 |
| 20070208245 | Brauker et al. | Sep 2007 | A1 |
| 20070208246 | Brauker et al. | Sep 2007 | A1 |
| 20070213610 | Say et al. | Sep 2007 | A1 |
| 20070232876 | Otto et al. | Oct 2007 | A1 |
| 20070235331 | Simpson et al. | Oct 2007 | A1 |
| 20080021666 | Goode, Jr. et al. | Jan 2008 | A1 |
| 20080033254 | Kamath et al. | Feb 2008 | A1 |
| 20080071157 | McGarraugh et al. | Mar 2008 | A1 |
| 20080071158 | McGarraugh et al. | Mar 2008 | A1 |
| 20080072663 | Keenan et al. | Mar 2008 | A1 |
| 20080154101 | Jain et al. | Jun 2008 | A1 |
| 20080183061 | Goode et al. | Jul 2008 | A1 |
| 20080183399 | Goode et al. | Jul 2008 | A1 |
| 20080187655 | Markle et al. | Aug 2008 | A1 |
| 20080188722 | Markle et al. | Aug 2008 | A1 |
| 20080188725 | Markle et al. | Aug 2008 | A1 |
| 20080189051 | Goode et al. | Aug 2008 | A1 |
| 20080193936 | Squirrell | Aug 2008 | A1 |
| 20080194837 | Kim et al. | Aug 2008 | A1 |
| 20080194936 | Goode et al. | Aug 2008 | A1 |
| 20080194937 | Goode et al. | Aug 2008 | A1 |
| 20080195967 | Goode et al. | Aug 2008 | A1 |
| 20080287764 | Rasdal et al. | Nov 2008 | A1 |
| 20080287765 | Rasdal et al. | Nov 2008 | A1 |
| 20080287766 | Rasdal et al. | Nov 2008 | A1 |
| 20080305009 | Gamsey et al. | Dec 2008 | A1 |
| 20080305506 | Suri | Dec 2008 | A1 |
| 20080306368 | Goode, Jr. et al. | Dec 2008 | A1 |
| 20080306433 | Cesaroni | Dec 2008 | A1 |
| 20080306434 | Dobbles et al. | Dec 2008 | A1 |
| 20080306435 | Kamath et al. | Dec 2008 | A1 |
| 20080306444 | Brister et al. | Dec 2008 | A1 |
| 20090005666 | Shin et al. | Jan 2009 | A1 |
| 20090012379 | Goode, Jr. et al. | Jan 2009 | A1 |
| 20090018418 | Markle et al. | Jan 2009 | A1 |
| 20090018426 | Markle et al. | Jan 2009 | A1 |
| 20090036758 | Brauker et al. | Feb 2009 | A1 |
| 20090043181 | Brauker et al. | Feb 2009 | A1 |
| 20090043182 | Brauker et al. | Feb 2009 | A1 |
| 20090043525 | Brauker et al. | Feb 2009 | A1 |
| 20090043541 | Brauker et al. | Feb 2009 | A1 |
| 20090043542 | Brauker et al. | Feb 2009 | A1 |
| 20090061528 | Suri | Mar 2009 | A1 |
| 20090062633 | Brauker et al. | Mar 2009 | A1 |
| 20090062635 | Brauker et al. | Mar 2009 | A1 |
| 20090062645 | Fehre et al. | Mar 2009 | A1 |
| 20090076361 | Kamath et al. | Mar 2009 | A1 |
| 20090081803 | Gamsey et al. | Mar 2009 | A1 |
| 20090124877 | Goode, Jr. et al. | May 2009 | A1 |
| 20090124878 | Goode, Jr. et al. | May 2009 | A1 |
| 20090156924 | Shariati et al. | Jun 2009 | A1 |
| 20090177143 | Markle et al. | Jul 2009 | A1 |
| 20090182217 | Li et al. | Jul 2009 | A1 |
| 20090192366 | Mensinger et al. | Jul 2009 | A1 |
| 20090192380 | Shariati et al. | Jul 2009 | A1 |
| 20090192722 | Shariati et al. | Jul 2009 | A1 |
| 20090192724 | Brauker et al. | Jul 2009 | A1 |
| 20090192745 | Kamath et al. | Jul 2009 | A1 |
| 20090192751 | Kamath et al. | Jul 2009 | A1 |
| 20090203981 | Brauker et al. | Aug 2009 | A1 |
| 20090204341 | Brauker et al. | Aug 2009 | A1 |
| 20090216103 | Brister et al. | Aug 2009 | A1 |
| 20090240120 | Mensinger et al. | Sep 2009 | A1 |
| 20090240128 | Mensinger et al. | Sep 2009 | A1 |
| 20090240193 | Mensinger et al. | Sep 2009 | A1 |
| 20090242399 | Kamath et al. | Oct 2009 | A1 |
| 20090242425 | Kamath et al. | Oct 2009 | A1 |
| 20090247857 | Harper et al. | Oct 2009 | A1 |
| 20090264719 | Markle et al. | Oct 2009 | A1 |
| 20090264856 | Lebel et al. | Oct 2009 | A1 |
| 20090287074 | Shults et al. | Nov 2009 | A1 |
| 20090299162 | Brauker et al. | Dec 2009 | A1 |
| 20090299276 | Brauker et al. | Dec 2009 | A1 |
| 20100010324 | Brauker et al. | Jan 2010 | A1 |
| 20100010331 | Brauker et al. | Jan 2010 | A1 |
| 20100010332 | Brauker et al. | Jan 2010 | A1 |
| 20100016687 | Brauker et al. | Jan 2010 | A1 |
| 20100022855 | Brauker et al. | Jan 2010 | A1 |
| 20100030053 | Goode, Jr. et al. | Feb 2010 | A1 |
| 20100030484 | Brauker et al. | Feb 2010 | A1 |
| 20100030485 | Brauker et al. | Feb 2010 | A1 |
| 20100036215 | Goode, Jr. et al. | Feb 2010 | A1 |
| 20100036216 | Goode, Jr. et al. | Feb 2010 | A1 |
| 20100036222 | Goode, Jr. et al. | Feb 2010 | A1 |
| 20100036223 | Goode, Jr. et al. | Feb 2010 | A1 |
| 20100036224 | Goode, Jr. et al. | Feb 2010 | A1 |
| 20100036225 | Goode, Jr. et al. | Feb 2010 | A1 |
| 20100045465 | Brauker et al. | Feb 2010 | A1 |
| 20100081908 | Dobbles et al. | Apr 2010 | A1 |
| 20100161269 | Kamath et al. | Jun 2010 | A1 |
| 20100174158 | Kamath et al. | Jul 2010 | A1 |
| 20100174167 | Kamath et al. | Jul 2010 | A1 |
| 20100174168 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100179399 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100179405 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100179406 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100179407 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100179408 | Kamath et al. | Jul 2010 | A1 |
| 20100179409 | Kamath et al. | Jul 2010 | A1 |
| 20100185065 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100185072 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100185073 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100185074 | Goode, Jr. et al. | Jul 2010 | A1 |
| 20100204555 | Shults et al. | Aug 2010 | A1 |
| 20100214104 | Goode, Jr. et al. | Aug 2010 | A1 |
| 20100217106 | Goode, Jr. et al. | Aug 2010 | A1 |
| 20100217555 | Kamath et al. | Aug 2010 | A1 |
| 20100234707 | Goode, Jr. et al. | Sep 2010 | A1 |
| 20100234796 | Kamath et al. | Sep 2010 | A1 |
| 20100235106 | Kamath et al. | Sep 2010 | A1 |
| 20100240975 | Goode, Jr. et al. | Sep 2010 | A1 |
| 20100240976 | Goode, Jr. et al. | Sep 2010 | A1 |
| 20110118579 | Goode, Jr. et al. | May 2011 | A1 |
| 20110124997 | Goode, Jr. et al. | May 2011 | A1 |
| 20110125410 | Goode, Jr. et al. | May 2011 | A1 |
| 20110130970 | Goode, Jr. et al. | Jun 2011 | A1 |
| 20110137601 | Goode, Jr. et al. | Jun 2011 | A1 |
| 20110178378 | Brister et al. | Jul 2011 | A1 |
| 20110190614 | Brister et al. | Aug 2011 | A1 |
| 20110218414 | Kamath et al. | Sep 2011 | A1 |
| 20110231140 | Goode, Jr. et al. | Sep 2011 | A1 |
| 20110231142 | Goode, Jr. et al. | Sep 2011 | A1 |
| 20110270062 | Goode, Jr. et al. | Nov 2011 | A1 |
| 20120215086 | Kamath et al. | Aug 2012 | A1 |
| 20140121989 | Kamath et al. | May 2014 | A1 |
| 20150289819 | Kamath et al. | Oct 2015 | A1 |
| 20170128021 | Kamath et al. | May 2017 | A1 |
| 20170245801 | Goode et al. | Aug 2017 | A1 |
| 20170245802 | Goode et al. | Aug 2017 | A1 |
| 20180168513 | Goode et al. | Jun 2018 | A1 |
| 20180271450 | Kamath et al. | Sep 2018 | A1 |
| 20180279959 | Kamath et al. | Oct 2018 | A1 |
| 20180289333 | Kamath et al. | Oct 2018 | A1 |
| 20180296164 | Goode et al. | Oct 2018 | A1 |
| 20190209095 | Kamath et al. | Jul 2019 | A1 |
| Number | Date | Country |
|---|---|---|
| 2127172 | Jul 1998 | CA |
| 0098592 | Jan 1984 | EP |
| 0107634 | May 1984 | EP |
| 0127958 | Dec 1984 | EP |
| 0230472 | Aug 1987 | EP |
| 0286118 | Oct 1988 | EP |
| 0288793 | Nov 1988 | EP |
| 0320109 | Jun 1989 | EP |
| 0352610 | Jan 1990 | EP |
| 0352631 | Jan 1990 | EP |
| 0353328 | Feb 1990 | EP |
| 0390390 | Oct 1990 | EP |
| 0406473 | Jan 1991 | EP |
| 0440044 | Aug 1991 | EP |
| 0441252 | Aug 1991 | EP |
| 0467078 | Jan 1992 | EP |
| 0534074 | Mar 1993 | EP |
| 0535898 | Apr 1993 | EP |
| 0563795 | Oct 1993 | EP |
| 0323605 | Jan 1994 | EP |
| 0286118 | Jan 1995 | EP |
| 0647849 | Apr 1995 | EP |
| 0424633 | Jan 1996 | EP |
| 0535898 | Feb 1997 | EP |
| 0776628 | Jun 1997 | EP |
| 0817809 | Jan 1998 | EP |
| 0838230 | Apr 1998 | EP |
| 0880936 | Dec 1998 | EP |
| 0885932 | Dec 1998 | EP |
| 0995805 | Apr 2000 | EP |
| 1077634 | Feb 2001 | EP |
| 1078258 | Feb 2001 | EP |
| 1153571 | Nov 2001 | EP |
| 0817809 | Jul 2002 | EP |
| 1236995 | Sep 2002 | EP |
| 1077636 | Jan 2004 | EP |
| 2223710 | Sep 2010 | EP |
| 2226086 | Sep 2010 | EP |
| 2656423 | Jun 1991 | FR |
| 2760962 | Sep 1998 | FR |
| 1442303 | Jul 1976 | GB |
| 2149918 | Jun 1985 | GB |
| S6283849 | Apr 1987 | JP |
| 2002513602 | May 2002 | JP |
| WO-8902720 | Apr 1989 | WO |
| WO-8908713 | Sep 1989 | WO |
| WO-9000734 | Jan 1990 | WO |
| WO-9000738 | Jan 1990 | WO |
| WO-9010861 | Sep 1990 | WO |
| WO-9207525 | May 1992 | WO |
| WO-9213271 | Aug 1992 | WO |
| WO-9314693 | Aug 1993 | WO |
| WO-9319701 | Oct 1993 | WO |
| WO-9422367 | Oct 1994 | WO |
| WO-9502357 | Jan 1995 | WO |
| WO-9507109 | Mar 1995 | WO |
| WO-9600110 | Jan 1996 | WO |
| WO-9601611 | Jan 1996 | WO |
| WO-9614026 | May 1996 | WO |
| WO-9625089 | Aug 1996 | WO |
| WO-9630431 | Oct 1996 | WO |
| WO-9632076 | Oct 1996 | WO |
| WO-9636296 | Nov 1996 | WO |
| WO-9701986 | Jan 1997 | WO |
| WO-9717884 | May 1997 | WO |
| WO-9728737 | Aug 1997 | WO |
| WO-9743633 | Nov 1997 | WO |
| WO-9824358 | Jun 1998 | WO |
| WO-9842249 | Oct 1998 | WO |
| WO-9856293 | Dec 1998 | WO |
| WO-9932881 | Jul 1999 | WO |
| WO-9944508 | Sep 1999 | WO |
| WO-9948419 | Sep 1999 | WO |
| WO-9956613 | Nov 1999 | WO |
| WO-9958051 | Nov 1999 | WO |
| WO-9958973 | Nov 1999 | WO |
| WO-0012720 | Mar 2000 | WO |
| WO-0013002 | Mar 2000 | WO |
| WO-0013003 | Mar 2000 | WO |
| WO-0019887 | Apr 2000 | WO |
| WO-0032098 | Jun 2000 | WO |
| WO-0033065 | Jun 2000 | WO |
| WO-0049940 | Aug 2000 | WO |
| WO-0059373 | Oct 2000 | WO |
| WO-0074753 | Dec 2000 | WO |
| WO-0078210 | Dec 2000 | WO |
| WO-0112158 | Feb 2001 | WO |
| WO-0116579 | Mar 2001 | WO |
| WO-0120019 | Mar 2001 | WO |
| WO-0120334 | Mar 2001 | WO |
| WO-0134243 | May 2001 | WO |
| WO-0143660 | Jun 2001 | WO |
| WO-0152727 | Jul 2001 | WO |
| WO-0158348 | Aug 2001 | WO |
| WO-0168901 | Sep 2001 | WO |
| WO-0169222 | Sep 2001 | WO |
| WO-0173109 | Oct 2001 | WO |
| WO-0188524 | Nov 2001 | WO |
| WO-0188534 | Nov 2001 | WO |
| WO-0205702 | Jan 2002 | WO |
| WO-0224065 | Mar 2002 | WO |
| WO-02053764 | Jul 2002 | WO |
| WO-02058537 | Aug 2002 | WO |
| WO-02082989 | Oct 2002 | WO |
| WO-02089666 | Nov 2002 | WO |
| WO-02100266 | Dec 2002 | WO |
| WO-03000127 | Jan 2003 | WO |
| WO-03012422 | Feb 2003 | WO |
| WO-03032411 | Apr 2003 | WO |
| WO-03101862 | Dec 2003 | WO |
| WO-2004052190 | Jun 2004 | WO |
| WO-2004060455 | Jul 2004 | WO |
| WO-2004098685 | Nov 2004 | WO |
| WO-2004110256 | Dec 2004 | WO |
| WO-2005011489 | Feb 2005 | WO |
| WO-2005012873 | Feb 2005 | WO |
| WO-2005026689 | Mar 2005 | WO |
| WO-2005026690 | Mar 2005 | WO |
| WO-2005032400 | Apr 2005 | WO |
| WO-2005057168 | Jun 2005 | WO |
| WO-2005057175 | Jun 2005 | WO |
| WO-2005078424 | Aug 2005 | WO |
| WO-2005026689 | Oct 2005 | WO |
| WO-2006050405 | May 2006 | WO |
| WO-2006105146 | Oct 2006 | WO |
| WO-2006118713 | Nov 2006 | WO |
| WO-2006131288 | Dec 2006 | WO |
| WO-2007002579 | Jan 2007 | WO |
| WO-2007065285 | Jun 2007 | WO |
| WO-2007114943 | Oct 2007 | WO |
| WO-2007127606 | Nov 2007 | WO |
| WO-2007143225 | Dec 2007 | WO |
| WO-2008076868 | Jun 2008 | WO |
| Entry |
|---|
| US 7,530,950 B2, 05/2009, Brister et al. (withdrawn) |
| US 7,530,950, 05/2009, Brister et al. (withdrawn) |
| Abbott Diabetes Care, Inc. Grounds of Opposition dated Mar. 30, 2011 against EP 1711802 (04813255.9), issued Jul. 14, 2010, 77 pages. |
| Ackerman N., et al., “Glucose Monitoring via Reverse Iontophoresis,” Controlled Drug Delivery, ACS Symposium Series, 2000, Chapter 27, pp. 273-282. |
| Adilman, et al., “Videogames: Knowing The Score, Creative Computing,” Dec. 1983, Dialog: File 148; IAC Trade & Industry Database, vol. 9, p. 224(5) (9 pages). |
| Asberg P., et al., “Hydrogels of a Conducting Conjugated Polymer as 3-D Enzyme Electrode,” Biosensors Bioelectronics, 2003, vol. 19, pp. 199-207. |
| Bard A.J., et al., “Electrochemical Methods,” Fundamentals and Applications, 2nd edition, Chapter 5-6, John Wiley & Sons, Inc., 2001, pp. 156-260. |
| Brauker, et al., “Sustained Expression of High Levels of Human Factor IX from Human Cells Implanted within an Immunoisolation Device into Athymic Rodents,” Human Gene Therapy, Apr. 10, 1998, vol. 9, pp. 879-888. |
| Dai W.S., et al., “Hydrogel Membranes with Mesh Size Asymmetry based on the Gradient Crosslinking of Poly(Vinyl Alcohol),” Journal of Membrane Science, 1999, vol. 156, pp. 67-79. |
| European Electronic File History for EP Application No. 10195447.7, filed Jul. 13, 2005—withdrawn, 200 pages. |
| European Electronic File History for EP Application No. 10195504.5, filed Jul. 13, 2005, 184 pages—withdrawn. |
| European Notices of Opposition dated May 23, 2011 filed by Abbott Diabetes Care, Inc. and Roche Diagnostics against EP Patent No. 1711802 (04813255.9), dated Jul. 14, 2010, 28 pages. |
| European Reply by Patent Proprietor dated Dec. 2, 2011 to Oppositions against EP Patent No. 1711802 (04813255.9), dated Jul. 14, 2010, 18 pages. |
| Extended European Search Report for Application No. 10193214.3 dated Mar. 11, 2011, 6 pages. |
| Extended European Search Report for Application No. 10193216.8 dated Apr. 14, 2011, 6 pages. |
| Extended European Search Report for Application No. 10193217.6 dated Mar. 1, 2011, 6 pages. |
| Extended European Search Report for Application No. 10195483.2 dated Apr. 15, 2011, 6 pages. |
| Extended European Search Report for Application No. 10195496.4 dated Apr. 1, 2011, 6 pages. |
| Extended European Search Report for Application No. 10195508.6 dated Aug. 2, 2011, 9 pages. |
| Extended European Search Report for Application No. 10195509.4 dated Apr. 12, 2011, 6 pages. |
| Extended European Search Report for Application No. 10195514.4 dated Apr. 21, 2011, 7 pages. |
| Extended European Search Report for Application No. 10195517.7 dated Apr. 7, 2011, 6 pages. |
| Extended European Search Report for Application No. 10195518.5 dated Aug. 10, 2011, 9 pages. |
| Extended European Search Report for Application No. 18204725.8 dated May 13, 2019, 5 pages. |
| Extended European Search Report for Application No. 6824880.6 dated Sep. 13, 2012, 6 pages. |
| Feldman B., et al., “Correlation of Glucose Concentrations in Interstitial Fluid and Venous Blood During Periods of Rapid Glucose Change,” Abbott Diabetes Care, Inc. Freestyle Navigator Continuous Glucose Monitor Pamphlet, 2004, 1 page. |
| File History of U.S. Appl. No. 09/447,227, filed Nov. 22, 1999, 1184 pages. |
| File History of U.S. Appl. No. 10/632,537, filed Aug. 1, 2003, 211 pages. |
| File History of U.S. Appl. No. 10/633,329, filed Aug. 1, 2003, 711 pages. |
| File History of U.S. Appl. No. 10/633,367, filed Aug. 1, 2003, 432 pages. |
| File History of U.S. Appl. No. 10/633,404, filed Aug. 1, 2003, 270 pages. |
| File History of U.S. Appl. No. 10/646,333, filed Aug. 22, 2003, 303 pages. |
| File History of U.S. Appl. No. 10/647,065, filed Aug. 22, 2003, 203 pages. |
| File History of U.S. Appl. No. 10/695,636, filed Oct. 28, 2003, 194 pages. |
| File History of U.S. Appl. No. 10/789,359, filed Feb. 26, 2004, 361 pages. |
| File History of U.S. Appl. No. 10/838,658, filed May 3, 2004, 748 pages. |
| File History of U.S. Appl. No. 10/838,909, filed May 3, 2004, 356 pages. |
| File History of U.S. Appl. No. 10/838,912, filed May 3, 2004, 1288 pages. |
| File History of U.S. Appl. No. 10/842,716, filed May 10, 2004, 670 pages. |
| File History of U.S. Appl. No. 10/846,150, filed May 14, 2004, 382 pages. |
| File History of U.S. Appl. No. 10/885,476, filed Jul. 6, 2004, 226 pages. |
| File History of U.S. Appl. No. 10/896,312, filed Jul. 20, 2004, 237 pages. |
| File History of U.S. Appl. No. 10/896,637, filed Jul. 21, 2004, 295 pages. |
| File History of U.S. Appl. No. 10/896,639, filed Jul. 21, 2004, 337 pages. |
| File History of U.S. Appl. No. 10/896,772, filed Jul. 21, 2004, 210 pages. |
| File History of U.S. Appl. No. 10/897,377, filed Jul. 21, 2004, 178 pages. |
| File History of U.S. Appl. No. 11/007,920, filed Dec. 8, 2004, 960 pages. |
| File History of U.S. Appl. No. 11/077,643, filed Mar. 10, 2005, 253 pages. |
| File History of U.S. Appl. No. 11/077,693, filed Mar. 10, 2005, 755 pages. |
| File History of U.S. Appl. No. 11/077,713, filed Mar. 10, 2005, 1025 pages. |
| File History of U.S. Appl. No. 11/077,714, filed Mar. 10, 2005, 320 pages. |
| File History of U.S. Appl. No. 11/077,715, filed Mar. 10, 2005, 717 pages. |
| File History of U.S. Appl. No. 11/077,739, filed Mar. 10, 2005, 778 pages. |
| File History of U.S. Appl. No. 11/077,740, filed Mar. 10, 2005, 921 pages. |
| File History of U.S. Appl. No. 11/077,763, filed Mar. 10, 2005, 515 pages. |
| File History of U.S. Appl. No. 11/077,765, filed Mar. 10, 2005, 932 pages. |
| File History of U.S. Appl. No. 11/077,883, filed Mar. 10, 2005, 1159 pages. |
| File History of U.S. Appl. No. 11/078,072, filed Mar. 10, 2005, 1334 pages. |
| File History of U.S. Appl. No. 11/078,230, filed Mar. 10, 2005, 535 pages. |
| File History of U.S. Appl. No. 11/078,232, filed Mar. 10, 2005, 256 pages. |
| File History of U.S. Appl. No. 11/157,365, filed Jun. 21, 2005, 977 pages. |
| File History of U.S. Appl. No. 11/157,746, filed Jun. 21, 2005, 603 pages. |
| File History of U.S. Appl. No. 11/158,227, filed Jun. 21, 2005, 474 pages. |
| File History of U.S. Appl. No. 11/201,445, filed Aug. 10, 2005, 120 pages. |
| File History of U.S. Appl. No. 12/052,489, filed Mar. 20, 2008, 238 pages. |
| File History of U.S. Appl. No. 12/367,468, filed Feb. 6, 2009, 228 pages |
| Geller R.I., et al., “Use of an Immunoisolation Device for Cell Transplantation and Tumor Immunotherapy,” Annals of the New York Academy of Science, 1997, vol. 831, pp. 438-451. |
| Gerritsen M., et al., “Influence of Inflammatory Cells and Serum on the Performance of Implantable Glucose Sensors,” Journal of Biomedical Material Research, 2001, vol. 54, pp. 69-75. |
| Gough D.A., “The implantable Glucose Sensor: An Example of Bioengineering Design,” Introduction to Bioengineering, 2001, Chapter 3, pp. 57-66. |
| Gregg B A., et al., “Cross-Linked Redox Gels Containing Glucose Oxidase for Amperometric Biosensor Applications,” Anal Chem, 1990, vol. 62, pp. 258-263. |
| International Preliminary Examination Report on for Application No. PCT/US2001/023850 dated Jun. 4, 2003, 3 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2004/024263 dated Feb. 6, 2006, 5 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2004/041095 dated Jun. 12, 2006, 4 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2005/024993 dated Jan. 16, 2007, 7 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2006/031496 dated Nov. 27, 2008, 8 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2007/005422 dated Sep. 1, 2009, 5 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2008/058158 dated Sep. 29, 2009, 9 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2004/024263, dated Nov. 29, 2004, 6 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2004/038724, dated Jan. 9, 2006, 4 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2004/041095, dated Jun. 1, 2005, 5 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2005/024993, dated Nov. 4, 2005, 12 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2006/031496 dated Sep. 20, 2007, 9 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2006/034284 dated Aug. 17, 2007, 8 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2007/005422 dated May 20, 2008, 5 pages. |
| International Search Report for Application No. PCT/US2001/023850 dated Jan. 16, 2002, 3 pages. |
| Kurnik R.T., et al., “Design and Simulation of a Reverse Iontophoretic Glucose Monitoring Device,” Journal of Electrochemical Society, 1998, vol. 145 (12), pp. 4119-4125. |
| Lee E., et al., “Effects of Pore Size, Void Volume, and Pore Connectivity on Tissue Responses to Porous Silicone Implants,” Society for Biomaterials, 25th Annual Meeting, 1999, p. 171. |
| Miller K.M., et al., “Generation of IL-1 like Activity in Response to Biomedical Polymer Implants: a Comparison of in Vitro and in Vivo Models,” Journal of Biomedical Materials Research, vol. 23(9), 1989, pp. 1007-1026. |
| Miller K.M., et al., “Human monocyte/macrophage activation and interleukin 1 generation by biomedical polymers,” Journal of Biomedical Materials Research, vol. 22 (8), 1988, pp. 713-731. |
| Miller K.M., et al., “In Vitro Stimulation of Fibroblast Activity by Factors Generated from Human Monocytes Activated by Biomedical Polymers,” Journal of Biomedical Materials Research, vol. 23(8), 1989, pp. 911-930. |
| Mowery K.A., et al., “Preparation and Characterization by Hydrophobic Polymeric Films that are Thromboresistant via Nitric Oxide Release,” Biomaterials, vol. 21, 2000, pp. 9-21. |
| Nam Y.S., et al., “A Novel Fabrication Method of Macroporous Biodegradable Polymer Scaffolds Using Gas Foaming Salt as a Porogen Additive,” J Biomed Mater Res, 2000, vol. 53, pp. 1-7. |
| Newman, J.D., et al., “Catalytic Materials, Membranes, and Fabrication Technologies Suitable for the Construction of Amperometric Biosensors,” Analytical Chemistry, 1998, vol. 67, pp. 4594-4599. |
| Office Action for U.S. Appl. No. 11/038,340, dated Feb. 2, 2010, 18 pages. |
| Office Action for U.S. Appl. No. 11/038,340, dated Jun. 7, 2010, 18 pages. |
| Office Action for U.S. Appl. No. 11/280,672, dated Oct. 29, 2009, 19 pages. |
| Office Action for U.S. Appl. No. 11/360,819, dated Sep. 2, 2010, 8 pages. |
| Office Action for U.S. Appl. No. 11/373,628, dated Aug. 10, 2010, 10 pages. |
| Office Action for U.S. Appl. No. 12/055,098, dated Oct. 5, 2010, 12 pages. |
| Office Action for U.S. Appl. No. 12/098,353, dated Aug. 26, 2010, 16 pages. |
| Office Action for U.S. Appl. No. 12/098,359, dated Jul. 7, 2010, 18 pages. |
| Office Action for U.S. Appl. No. 12/102,729, dated Jul. 7, 2009, 7 pages. |
| Office Action for U.S. Appl. No. 12/133,738, dated Sep. 10, 2010, 11 pages. |
| Office Action for U.S. Appl. No. 12/133,761, dated Sep. 7, 2010, 11 pages. |
| Office Action for U.S. Appl. No. 12/182,008, dated Aug. 24, 2010, 15 pages. |
| Office Action for U.S. Appl. No. 12/182,073, dated Jun. 28, 2010, 20 pages. |
| Office Action for U.S. Appl. No. 12/182,083, dated Jun. 24, 2010, 8 pages. |
| Office Action for U.S. Appl. No. 12/364,786, dated Jul. 29, 2010, 6 pages. |
| Office Action for U.S. Appl. No. 12/536,852, dated Jun. 25, 2010, 8 pages. |
| Office Action for U.S. Appl. No. 12/536,852, dated Oct. 18, 2010, 10 pages. |
| Office Action for U.S. Appl. No. 12/619,502, dated Sep. 7, 2010, 6 pages. |
| Office Action for U.S. Appl. No. 95/001,038, dated May 28, 2010, 32 pages. |
| Office Action from European Patent Application No. 04813418.3, dated Sep. 22, 2010, 4 pages. |
| Office Action from European Patent Application No. 05723951.9, dated Sep. 7, 2010, 5 pages. |
| Office Action from European Patent Application No. 05771643.3, dated Aug. 19, 2009, 3 pages. |
| Office Action from European Patent Application No. 05771646.6, dated Aug. 19, 2009, 4 pages. |
| Office Action from European Patent Application No. 05771646.6, dated Jun. 2, 2010, 5 pages. |
| Office Action from European Patent Application No. 10195511.0, dated May 12, 2011, 5 pages. |
| Office Action from European Patent Application No. 10195518.5, dated Apr. 28, 2011, 3 pages. |
| Office Action from Japanese Patent Application No. 2006-522016 dated Aug. 31, 2010, 6 pages. |
| Office Action from Japanese Patent Application No. 2007-521636 dated Mar. 1, 2011, 4 pages. |
| Ratner B.D., “Reducing Capsular Thickness and Enhancing Angiogenesis around Implant Drug Release Systems,” Journal of Controlled Release, vol. 78, 2002, pp. 211-218. |
| Roche Diagnostics GmbH Grounds of Opposition dated Apr. 14, 2011 against EP 1711802 (04813255.9), issued Jul. 14, 2010. |
| Sieminski, et al., “Biomaterial-Microvasculature Interactions,” Biomaterials, 2000, vol. 21, pp. 2233-2241. |
| Sigma-Aldrich Corp., “Cellulose Acetate,” Product Description, Product No. 419028, St. Louis, MO, 2005, 1 page. |
| Sternberg, et al., “Covalent Enzyme Coupling on Cellulose Acetate Membranes for Glucose Sensor Development,” Anal Chem, Dec. 1988, vol. 60(24), pp. 2781-2786. |
| Tang, et al., “Fibrin(ogen) Mediates Acute Inflammatory Responses to Biomaterials,” J.Exp.Med, 1993, vol. 178, pp. 2147-2156. |
| Tang, et al., “Inflammatory Responses to Biomaterials,” Am J Clin Pathol, 1995, vol. 103, pp. 466-471. |
| Tang, et al., “Mast Cells Mediate Acute Inflammatory Responses to Implanted Biomaterials,” Proceedings of the National Academy of Sciences of the USA, 1998, vol. 95, pp. 8841-8846. |
| Tang, et al., “Molecular Determinants of Acute Inflammatory Responses to Biomaterials,” J Clin Invest, 1996, vol. 97, pp. 1329-1334. |
| Tibell, et al., “Survival of Macroencapsulated Allogeneic Parathyroid Tissue One Year after Transplantation in Nonimmunosuppressed Humans,” Cell Transplantation, 2001, vol. 10, pp. 591-599. |
| Vassilyev Y.B., et al., “Kinetics and Mechanism of Glucose Electrooxidation on Different Electrode-Catalysts: Part I. Adsorption and Oxidation on Platinum,” Journal of Electroanalytical Chemistry, 1985, vol. 196, pp. 105-125. |
| Wade L.G., “Reactions of Aromatic Compounds,” Organic Chemistry, Chapter 17, 5th edition, 2003, pp. 762-763. |
| Wang J., “Glucose Biosensors: 40 Years of Advances and Challenges,” Electroanalysis, 2001, vol. 13(12), pp. 983-988. |
| Aalders, et al., “Development of a Wearable Glucose Sensor; Studies in Healthy Volunteers and in Diabetic Patients,” The International Journal of Artificial Organs, 1991, vol. 14, No. 2, pp. 102-108. |
| Abe, et al., “Characterization of Glucose Microsensors for Intracellular Measurements,” Analytical Chemistry, 1992, vol. 64, No. 18, pp. 2160-2163. |
| Abel, et al., “Biosensors for in Vivo Glucose Measurements: Can We Cross the Experimental Stage,” Biosensors & Bioelectronics, 2002, vol. 17, pp. 1059-1070. |
| Abel, et al., “Experience With An Implantable Glucose Sensor as a Prerequisite of an Artificial Beta Cell,” Biomed. Biochim. Actan, 1984, vol. 43, No. 5, pp. 577-584. |
| Alcock S.J., et al., “Continuous Analyte Monitoring to Aid Clinical Practice,” IEEE Engineering in Medicine & Biology, 1994, vol. 13, pp. 319-325. |
| Amer M.M.B., “An Accurate Amperometric Glucose Sensor Based Glucometer with Eliminated Cross-Sensitivity,” Journal of Medical Engineering & Technology, vol. 26 (5), Sep./Oct. 2002, pp. 208-213. |
| Amin, et al., “Hypoglycemia Prevalence in Prepubertal Children With Type 1 Diabetes On Standard Insulin Regimen: Use of Continuous Glucose Monitoring System,” Diabetes Care, 2003, vol. 26, No. 3, pp. 662-667. |
| Armour J.C., et al., “Application of Chronic Intravascular Blood Glucose Sensor in Dogs,” Diabetes, Dec. 1990, vol. 39, pp. 1519-1526. |
| Atanasov P., et al., “Biosensor for Continuous Glucose Monitoring,” Biotechnology and Bioengineering, John Wiley & sons Inc, 1994, vol. 43, pp. 262-266. |
| Atanasov P., et al., “Implantation of a Refillable Glucose Monitoring-Telemetry Device,” Biosenors and Bioelectronics, vol. 12 (7), 1997, pp. 669-680. |
| Aussedat B., et al., “A User-Friendly Method For Calibrating a Subcutaneous Glucose Sensor-Based Hypoglycaemic Alarm,” Elsevier Science Limited, Biosensors & Bioelectronic, 1997, vol. 12, No. 11, pp. 1061-1071. |
| Bailey T.S., et al., “Reduction in Hemoglobin A1C with Real-Time Continuous Glucose Monitoring: Results from a 12-Week Observational Study,” Diabetes Technology & Therapeutics, vol. 9 (3), 2007, pp. 203-210. |
| Baker D.A., et al., “Dynamic Concentration Challenges for Biosensor Characterization,” Biosensors & Bioelectronics, vol. 8, 1993, pp. 433-441. |
| Baker D.A., et al., “Dynamic Delay and Maximal Dynamic Error in Continuous Biosensors,” Analytical Chemistry, vol. 68 (8), Apr. 15, 1996, pp. 1292-1297. |
| Bard A.J., et al., “Electrochemical Methods,” Fundamentals and Applications, John Wiley & Sons, New York, 1980, pp. 173-175. |
| Beach R.D., et al., “Subminiature Implantable Potentiostat and Modified Commercial Telemetry Device for Remote Glucose Monitoring,” IEEE Transactions on Instrumentation and Measurement, vol. 48 (6), Dec. 1999, pp. 1239-1245. |
| Bellucci F., et al., “Electrochemical Behaviour of Graphite-Epoxy Composite Materials (GECM) in Aqueous Salt Solutions,” Journal of Applied Electrochemistry, vol. 16 (1), Jan. 1986, pp. 15-22. |
| Bessman S.P., et al., “Progress toward a Glucose Sensor for the Artificial Pancreas,” Proceedings of a Workshop on Ion-Selective Microelectrodes, Jun. 4-5, 1973, Boston University, 1973, pp. 189-197. |
| Biermann E., et al., “How Would Patients Behave if they were Continually Informed of their Blood Glucose Levels? A Simulation Study Using a “Virtual” Patient,” Diabetes Technology & Therapeutics, vol. 10 (3), 2008, pp. 178-187. |
| Bindra D.S., et al., “Design and in Vitro Studies of a Needle-Type Glucose Sensor for Subcutaneous Monitoring,” Analytical Chemistry, vol. 63, Sep. 1, 1991, pp. 1692-1696. |
| Bindra D.S., et al., “Pulsed Amperometric Detection of Glucose in Biological Fluids at a Surface-Modified Gold Electrode,” Analytical Chemistry, vol. 61 (22), Nov. 15, 1989, pp. 2566-2570. |
| Bisenberger M., et al., “A Triple-Step Potential Waveform at Enzyme Multisensors with Thick-Film Gold Electrodes for Detection of Glucose and Sucrose,” Sensors and Actuators B, vol. 28, 1995, pp. 181-189. |
| Bland J.M., et al., “A Note on the Use of the Intraclass Correlation Coefficient in the Evaluation of Agreement between Two Methods of Measurement,” Computers in Biology and Medicine, vol. 20 (5), 1990, pp. 337-340. |
| Bland J.M., et al., “Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement,” The Lancet, Feb. 8, 1986, pp. 307-310. |
| Bobbioni-Harsch E., et al., “Lifespan of Subcutaneous Glucose Sensors and their Performances during Dynamic Glycaemia Changes in Rats,” J. Biomed. Eng., vol. 15, 1993, pp. 457-463. |
| Bode B.W., “Clinical Utility of the Continuous Glucose Monitoring System,” Diabetes Technology & Therapeutics, vol. 2, Supplement 1,2000, pp. S35-S41. |
| Bode B.W., et al., “Continuous Glucose Monitoring Used to Adjust Diabetes Therapy Improves Glycosylated Hemoglobin: A Pilot Study,” Diabetes Research and Clinical Practice, vol. 46, 1999, pp. 183-190. |
| Bode B.W., et al., “Using the Continuous Glucose Monitoring System to Improve the Management of Type 1 Diabetes,” Diabetes Technology & Therapeutics, vol. 2, Supplement 1, 2000, pp. S43-S48. |
| Boedeker Plastics Inc, “Polyethylene Specifications,” Polyethylene Data Sheet, Retrieved from http://www.boedeker.com/polye.sub.--p.htm on Aug. 19, 2009, 4 pages. |
| Boland E., et al., “Limitations of Conventional Methods of Self-Monitoring of Blood Glucose,” Diabetes Care, vol. 24 (11), Nov. 2001, pp. 1858-1862. |
| Bolinder J., et al., “Self-Monitoring of Blood Glucose in Type I Diabetic Patients: Comparison with Continuous Microdialysis Measurements of Glucose in Subcutaneous Adipose Tissue during Ordinary Life Conditions,” Diabetes Care, vol. 20 (1), Jan. 1997, pp. 64-70. |
| Bolinder J., et al., “Microdialysis Measurement of the Absolute Glucose Concentration in Subcutaneous Adipose Tissue Allowing Glucose Monitoring in Diabetic Patients,” Rapid Communication, Diabetologia, vol. 35, 1992, pp. 1177-1180. |
| Bott A.W., “A Comparison of Cyclic Voltammetry and Cyclic Staircase Voltammetry,” Current Separations, vol. 16 (1), 1997, pp. 23-26. |
| Bott A.W., “Electrochemical Methods for the Determination of Glucose,” Current Separations, vol. 17 (1), 1998, pp. 25-31. |
| Bowman L., et al., “The Packaging of Implantable Integrated Sensors,” IEEE Transactions in Biomedical Engineering, vol. BME-33 (2), Feb. 1986, pp. 248-255. |
| Brauker J., et al., “Local Inflammatory Response Around Diffusion Chambers Containing Xenografts,” Transplantation, vol. 61 (12), Jun. 27, 1996, pp. 1671-1677. |
| Braunwald E., “Biomarkers in Heart Failure,” Medical Progress, The New England Journal of Medicine, vol. 358, May 15, 2008, pp. 2148-2159. |
| Bremer T., et al., “Is Blood Glucose Predictable from Previous Values? A Solicitation for Data,” Perspectives in Diabetes, vol. 48, Mar. 1999, pp. 445-451. |
| Bremer T.M., et al., “Benchmark Data from the Literature for Evaluation of New Glucose Sensing Technologies,” Diabetes Technology & Therapeutics, vol. 3 (3), 2001, pp. 409-418. |
| Brooks S.L., et al., “Development of an On-line Glucose Sensor for Fermentation Monitoring,” Biosensors, vol. 3, 1987/1988, pp. 45-56. |
| Bruckel J., et al., “In Vivo Measurement of Subcutaneous Glucose Concentrations with an Enzymatic Glucose Sensor and a Wick Method,” Klin Wochenschr, vol. 67, 1989, pp. 491-495. |
| Brunstein E., et al., “Preparation and Validation of Implantable Electrodes for the Measurement of Oxygen and Glucose,” Biomed Biochim. Acta, vol. 48 (11/12), 1989, pp. 911-917. |
| Cai Q., et al., “A Wireless, Remote Query Glucose Biosensor Based on a pH-Sensitive Polymer,” Analytical Chemistry, vol. 76 (14), Jul. 15, 2004, pp. 4038-4043. |
| Cameron T., et al., “Micromodular Implants to Provide Electrical Stimulation of Paralyzed Muscles and Limbs,” IEEE Transactions on Biomedical Engineering, vol. 44 (9), Sep. 1997, pp. 781-790. |
| Campanella L., et al., “Biosensor for Direct Determination of Glucose and Lactate in Undiluted Biological Fluids,” Biosensors & Bioelectronics, vol. 8, 1993, pp. 307-314. |
| Candas B., et al., “An Adaptive Plasma Glucose Controller Based on a Nonlinear Insulin/Glucose Model,” IEEE Transactions on Biomedical Engineering, vol. 41 (2), Feb. 1994, pp. 116-124. |
| Cass A.E.G., et al., “Ferrocene-Mediated Enzyme Electrodes for Amperometric Determination of Glucose,” Analytical Chemistry, vol. 56 (4), Apr. 1984, pp. 667-671. |
| Cassidy J.F., et al., “Novel Electrochemical Device for the Detection of Cholesterol or Glucose,” Analyst, vol. 118, Apr. 1993, pp. 415-418. |
| Chase H.P., et al., “Continuous Subcutaneous Glucose Monitoring in Children with Type 1 Diabetes,” Pediatrics, vol. 107 (2), Feb. 2001, pp. 222-226. |
| Chen T., et al., “Defining the Period of Recovery of the Glucose Concentration after its Local Perturbation by the Implantation of a Miniature Sensor,” Clinical Chemistry and Laboratory Medicine, vol. 40 (8), 2002, pp. 786-789. |
| Chia C.W., et al., “Glucose Sensors: Toward Closed Loop Insulin Delivery,” Endocrinology and Metabolism Clinics of North America, vol. 33, 2004, pp. 175-195. |
| Choleau C., et al., “Calibration of a Subcutaneous Amperometric Glucose Sensor Implanted for 7 Days in Diabetic Patients Part 2. Superiority of the One-point Calibration Method,” Biosensors and Bioelectronics, vol. 17 (8), 2002, pp. 647-654. |
| Choleau C., et al., “Calibration of a Subcutaneous Amperometric Glucose Sensor Part 1. Effect of Measurement Uncertainties on the Determination of Sensor Sensitivity and Background Current,” Biosensors and Bioelectronics, vol. 17, 2002, pp. 641-646. |
| CIBA Specialty Chemicals, “Ciba® IRGACURE® 2959,” Coating Effects Segment, Photoinitiator Product Description, Basel Switzerland, Apr. 2, 1998, 3 pages. |
| Claremont D.J., et al., “Potentially-Implantable, Ferrocene-Mediated Glucose Sensor,” Journal of Biomedical Engineering, vol. 8, Jul. 1986, pp. 272-274. |
| Claremont D.J., et al., “Subcutaneous Implantation of a Ferrocene-Mediated Glucose Sensor in Pigs,” Diabetologia, vol. 29, 1986, pp. 817-821. |
| Clark L.C., et al., “Configurational Cyclic Voltammetry: Increasing the Specificity and Reliability of Implanted Electrodes,” IEEE/Ninth Annual Conference of the Engineering in Medicine and Biollogy Society, 1987, pp. 0782-0783. |
| Clark L.C., et al., “Long-Term Stability of Electroenzymatic Glucose Sensors Implanted in Mice,” vol. XXXIV, Transactions—American Society for Artificial Internal Organs, 1988, vol. 34, pp. 259-265. |
| Clark L.C., et al., “One-Minute Electrochemical Enzymic Assay for Cholesterol in Biological Materials,” Clinical Chemistry, vol. 27 (12), 1981, pp. 1978-1982. |
| Clarke W.L., et al., “Evaluating Clinical Accuracy of Systems for Self Monitoring of Blood Glucose,” Technical Articles, Diabetes Care, vol. 10 (5), Sep.-Oct. 1987, pp. 622-628. |
| Colangelo V.J., et al., “Corrosion Rate Measurements in Vivo,” Journal of Biomedical Materials Research, vol. 1, 1967, pp. 405-414. |
| Colowick S.P., et al., “Methods in Enzymology,” vol. XLIV, Immobilized Enzymes, Edited by Mosbach K, New York Academic Press, 1976, 11 pages. |
| Cox D.J., et al., “Accuracy of Perceiving Blood Glucose in IDDM,” Diabetes Care, vol. 8 (6), Nov.-Dec. 1985, pp. 529-536. |
| Csoregi E., et al., “Amperometric Microbiosensors for Detection of Hydrogen Peroxide and Glucose Based on Peroxidase-Modified Carbon Fibers,” Electroanalysis, vol. 6, 1994, pp. 925-933. |
| Csoregi E., et al., “Design, Characterization and One-Point in Vivo Calibration of a Subcutaneously Implanted Glucose Electrode,” American Chemical Society, Analytical Chemistry, vol. 66 (19), Oct. 1, 1994, pp. 3131-3138. |
| Currie J.F., et al., “Novel Non-Intrusive Trans-Dermal Remote Wireless Micro-Fluidic Monitoring System Applied to Continuous Glucose and Lactate Assays for Casualty Care and Combat Readiness Assessment,” RTO HFM Symposium, RTO-MP-HFM-109, Aug. 16-18, 2004, pp. 24-1-24-18. |
| Danielsson B., et al., “Enzyme Thermistors,” Methods in Enzymology, vol. 137, 1988, pp. 181-197. |
| D'Arrigo, et al., “Porous-Si Based Bio Reactors for Glucose Monitoring and Drugs Production,” Proceedings of SPIE, 2003, vol. 4982, pp. 178-184. |
| Dassau E., et al., “In Silico Evaluation Platform for Artificial Pancreatic β-Cell Development—A Dynamic Simulator for Closed-Loop Control with Hardware-in-the-loop,” Diabetes Technology & Therapeutics, vol. 11 (3), 2009, pp. 1-8. |
| Davies M.L., et al., “Polymer Membranes in Clinical Sensor Applications,” An overview of membrane function, Biomaterials, vol. 13 (14), 1992, pp. 971-978. |
| Davis G., et al., “Bioelectrochemical Fuel Cell and Sensor Based on a Quinoprotein, Alcohol Dehydrogenase,” Enzyme and Microbial Technology, vol. 5 (5), Sep. 1983, pp. 383-388. |
| Deutsch T., et al., “Time Series Analysis and Control of Blood Glucose Levels in Diabetic Patients,” Computer Methods and Programs in Biomedicine, Elsevier Scientific Publishers, vol. 41, 1994, pp. 167-182. |
| Dixon B.M., et al., “Characterization in Vitro and in Vivo of the Oxygen Dependence of an Enzyme/Polymer Biosensor for Monitoring Brain Glucose,” Journal of Neuroscience Methods, vol. 119, 2002, pp. 135-142. |
| DuPont, “Dimension® AR Clinical Chemistry System,” The Chemistry Analyzer that Makes the most of your Time, Money and Effort, Dade International, Chemistry Systems, Newark, 1998, 18 pages. |
| Durliat H., et al., “Spectrophotometric and Electrochemical Determinations of L( +)-Lactate in Blood by Use of Lactate Dehydrogenase from Yeast,” Clinical Chemistry, vol. 22 (11), 1976, pp. 1802-1805. |
| Edwards Lifesciences, “Accuracy for You and Your Patients,” Marketing materials, 2002, 4 pages. |
| El Degheidy M.M., et al., “Optimization of an Implantable Coated Wire Glucose Sensor,” Journal of Biomedical Engineering, vol. 8, Apr. 1986, pp. 121-129. |
| ELCO Diagnostics Company, “Direct 30/30® Blood Glucose Sensor,” Markwell Medical Catalog, 1990, 1 page. |
| El-Khatib F.H., et al., “Adaptive Closed-Loop Control Provides Blood-Glucose Regulation Using Dual Subcutaneous Insulin and Glucagon Infusion in Diabetic Swine,” Journal of Diabetes Science and Technology, Diabetes Technology Society, vol. 1 (2), 2007, pp. 181-192. |
| El-Sa'ad L., et al., “Moisture Absorption by Epoxy Resins: The Reverse Thermal Effect,” Journal of Materials Science, vol. 25, 1990, pp. 3577-3582. |
| Ernst H., et al., “Reliable Glucose Monitoring Through the Use of Microsystem Technology,” Analytical Bioanalytical Chemistry, vol. 373, 2002, pp. 758-761. |
| Extended European Search Report for Application No. 8770744.4dated Apr. 7, 2011,5 pages. |
| Fabietti P.G., et al., “Clinical Validation of a New Control-Oriented Model of Insulin and Glucose Dynamics in Subjects with Type 1 Diabetes,” Diabetes Technology & Therapeutics, vol. 9 (4), 2007, pp. 327-338. |
| Fahy B.G., et al., “An Analysis: Hyperglycemic Intensive Care Patients Need Continuous Glucose Monitoring-Easier Said Than Done,” Journal of Diabetes Science and Technology, Diabetes Technology Society, vol. 2 (2), Mar. 2008, pp. 201-204. |
| Fare T.L., et al., “Functional Characterization of a Conducting Polymer-Based Immunoassay System,” Biosonsors & Bioelectronics, vol. 13 (3-4), 1998, pp. 459-470. |
| Feldman B., et al., “A Continuous Glucose Sensor Based on Wired EnzymeTM Technology-Results from a 3-Day Trial in Patients with Type 1 Diabetes,” Diabetes Technology & Therapeutics, vol. 5 (5), 2003, pp. 769-779. |
| File History of U.S. Appl. No. 10/648,849, filed Aug. 22, 2003, 803 pages. |
| File History of U.S. Appl. No. 11/498,410, filed Aug. 2, 2006, 699 pages. |
| File History of U.S. Appl. No. 11/515,443, filed Sep. 1, 2006, 451 pages. |
| File History of U.S. Appl. No. 12/353,787, filed Jan. 14, 2009, 486 pages. |
| File History of U.S. Appl. No. 12/353,799, filed Jan. 14, 2009, 361 pages. |
| File History of U.S. Appl. No. 12/579,339, filed Oct. 14, 2009, 360 pages. |
| File History of U.S. Appl. No. 12/579,357, filed Oct. 14, 2009, 476 pages. |
| File History of U.S. Appl. No. 12/579,363, filed Oct. 14, 2009, 370 pages. |
| File History of U.S. Appl. No. 12/579,374, filed Oct. 14, 2009, 411 pages. |
| File History of U.S. Appl. No. 12/579,385, filed Oct. 14, 2009, 558 pages. |
| File History of U.S. Appl. No. 12/579,388, filed Oct. 14, 2009, 450 pages. |
| File History of U.S. Appl. No. 12/579,392, filed Oct. 14, 2009, 392 pages. |
| File History of U.S. Appl. No. 12/731,980, filed Mar. 25, 2010, 350 pages. |
| File History of U.S. Appl. No. 12/787,217, filed May 25, 2010, 356 pages. |
| File History of U.S. Appl. No. 12/789,153, filed May 27, 2010, 461 pages. |
| File History of U.S. Appl. No. 12/791,686, filed Jun. 1, 2010, 494 pages. |
| File History of U.S. Appl. No. 12/791,791, filed Jun. 1, 2010, 600 pages. |
| Fischer U., et al., “Assessment of Subcutaneous Glucose Concentration: Validation of the Wick Technique as a Reference for Implanted Electrochemical Sensors in Normal and Diabetic Dogs,” Diabetologia, vol. 30, 1987, pp. 940-945. |
| Fischer U., et al., “Hypoglycaemia-Warning by Means of Subcutaneous Electrochemical Glucose Sensors: An Animal Study,” Horm. Metab. Res, vol. 27, 1995, p. 53. (Abstract Only). |
| Fischer U., et al., “Oxygen Tension at the Subcutaneous Implantation Site of Glucose Sensors,” Biomed. Biochim. Acta, vol. 48 (11/12), 1989, pp. 965-971. |
| Freedman D., et al., “Statistics,” Second Edition, W.W. Norton & Company, New York & London, 1991, p. 74 (3 pages). |
| Freiberger P., “Video Game Takes on Diabetes Superhero ‘Captain Novolin’ Offers Treatment Tips,” Fourth Edition, Jun. 26, 1992, Business Section, 2 pages. |
| Frohnauer M.K., et al., “Graphical Human Insulin Time-Activity Profiles Using Standardized Definitions,” Diabetes Technology & Therapeutics, vol. 3 (3), 2001, pp. 419-429. |
| Frost M.C., et al., “Implantable Chemical Sensors for Real-Time Clinical Monitoring: Progress and Challenges,” Current Opinion in Chemical Biology, Analytical Techniques, vol. 6, 2002, pp. 633-641. |
| Gabby R.A., et al., “Optical Coherence Tomography-Based Continuous Noninvasive Glucose Monitoring in Patients with Diabetes,” Diabetes Technology & Therapeutics, vol. 10, Nov. 3, 2008, pp. 188-193. |
| Ganesan N., et al., “Gold Layer-Based Dual Crosslinking Procedure of Glucose Oxidase with Ferrocene Monocarboxylic Acid Provides a Stable Biosensor,” Analytical Biochemistry, Notes & Tips, vol. 343, 2005, pp. 188-191. |
| Ganesh A., et al., “Evaluation of the VIA® Blood Chemistry Monitor for Glucose in Healthy and Diabetic Volunteers,” Journal of Diabetes Science and Technology, vol. 2 (2), Mar. 2008, pp. 182-193. |
| Garg S.K., et al., “Correlation of Fingerstick Blood Glucose Measurements With GlucoWatch Biographer Glucose Results in Young Subjects With Type 1 Diabetes,” Emerging Treatments and Technologies, Diabetes Care, vol. 22 (10), Oct. 1999, pp. 1708-1714. |
| Garg S.K., et al., “Improved Glucose Excursions Using an Implantable Real-Time Continuous Glucose Sensor in Adults With Type 1 Diabetes,” Emerging Treatments and Technologies, Diabetes Care, vol. 27 (3), 2004, pp. 734-738. |
| Gerritsen M., et al., “Performance of Subcutaneously Implanted Glucose Sensors for Continuous Monitoring,” The Netherlands Journal of Medicine, vol. 54, 1999, pp. 167-179. |
| Gerritsen M., et al., “Problems Associated with Subcutaneously Implanted Glucose Sensors,” Diabetes Care, vol. 23 (2), Feb. 2000, pp. 143-145. |
| Gilligan B.J., et al., “Evaluation of a Subcutaneous Glucose Sensor Out to 3 Months in a Dog Model” Diabetes Care, vol. 17 (8), Aug. 1994, pp. 882-887. |
| Gilligan B.J., et al., “Feasibility of Continuous Long-Term Glucose Monitoring from a Subcutaneous Glucose Sensor in Humans,” Diabetes Technology & Therapeutics, vol. 6 (3), 2004, pp. 378-386. |
| Godsland I.F., et al., “Maximizing the Success Rate of Minimal Model Insulin Sensivity Measurement in Humans: The Importance of Basal Glucose Levels,” The Biochemical Society and the Medical Research Society, Clinical Science, vol. 101, 2001, pp. 1-9. |
| Gouda M.D., et al., “Thermal Inactivation of Glucose Oxidase,” The Journal of Biological Chemistry, vol. 278 (27), Issue of Jul. 4, 2003, pp. 24324-24333. |
| Gough D.A., et al., “Frequency Characterization of Blood Glucose Dynamics,” Annals of Biomedical Engineering, vol. 31, 2003, pp. 91-97. |
| Gough D.A., et al., “Immobilized Glucose Oxidase in Implantable Glucose Sensor Technology,” Diabetes Technology & Therapeutics, vol. 2 (3), 2000, pp. 377-380. |
| Gross, et al., “Diabetes Technology & Therapeutics,” Letters to the Editor, Diabetes Technology & Therapeutics, vol. 3 (1), 2001, pp. 129-131. |
| Gross T.M., et al., “Efficacy and Reliability of the Continuous Glucose Monitoring System,” Diabetes Technology & Therapeutics, vol. 2, Supplement 1, 2000, pp. S19-S26. |
| Gross T.M., et al., “Performance Evaluation of the Minimed® Continuous Glucose Monitoring System During Patient Home Use,” Diabetes Technology & Therapeutics, vol. 2(1), 2000, pp. 49-56. |
| Guerci B., et al., “Clinical Performance of CGMS in Type 1 Diabetic Patients Treated by Continuous Subcutaneous Insulin Infusion Using Insulin Analogs,” Diabetes Care, vol. 26, 2003, pp. 582-589. |
| Hall S.B., et al., “Electrochemical Oxidation of Hydrogen Peroxide at Platinum Electrodes. Part 1. An Adsorption-controlled Mechanism,” Electrochimica Acta, vol. 43, Nos. 5/6, 1998, pp. 579-588. |
| Hall S.B., et al., “Electrochemical Oxidation of Hydrogen Peroxide at Platinum Electrodes. Part II: Effect of potential,” Electrochimica Acta, vol. 43 (14-15), 1998, pp. 2015-2024. |
| Hall S.B., et al., “Electrochemical Oxidation of Hydrogen Peroxide at Platinum Electrodes. Part III: Effect of Temperature,” Electrochimica Acta, vol. 44, 1999, pp. 2455-2462. |
| Hall S.B., et al., “Electrochemical Oxidation of Hydrogen Peroxide at Platinum Electrodes. Part IV: Phosphate Buffer Dependence,” Electrochimica Acta, vol. 44, 1999, pp. 4573-4582. |
| Hall S.B., et al., “Electrochemical Oxidation of Hydrogen Peroxide at Platinum Electrodes. Part V: Inhibition by Chloride,” Electrochimica Acta, vol. 45, 2000, pp. 3573-3579. |
| Hamilton, “Complete Guide to Selecting the Right Hamilton Gastight, Microliter, and Specialty Syringe for your Application,” Syringe Selection, www.hamiltoncompany.com, 2006, 20 pages. |
| Hashiguchi Y., et al., “Development of a Miniaturized Glucose Monitoring System by Combining a Needle-Type Glucose Sensor with Microdialysis Sampling Method: Long-term subcutaneous tissue glucose monitoring in ambulatory diabetic patients,” Diabetes Care, vol. 17, No. 5, May 1994, pp. 387-396. |
| Heise T., et al., “Hypoglycemia warning signal and glucose sensors: Requirements and concepts,” Diabetes Technology & Therapeutics, vol. 5, No. 4, 2003, pp. 563-571. |
| Heller A., “Electrical Connection of Enzyme Redox Centers to Electrodes,” J. Phys. Chem., vol. 96, 1992, pp. 3579-3587. |
| Heller A., “Electrical Wiring of Redox Enzymes,” Ace. Chem. Res., vol. 23, 1990, pp. 128-134. |
| Heller A., “Implanted Electrochemical Glucose Sensors for the Management of Diabetes,” Annu. Rev., Biomed Eng., vol. 1, 1999, pp. 153-175. |
| Heller A., “Plugging Metal Connectors into Enzymes,” Nature Biotechnology, vol. 21, No. 6, Jun. 2003, pp. 631-632. |
| Hicks J.M., “In Situ Monitoring,” Clinical Chemistry, vol. 31 (12), 1985, pp. 1931-1935. |
| Hitchman M.L., “Measurement of Dissolved Oxygen,” Edited by Elving P.J et al., Chemical Analysis, New York, John Wiley & Sons, vol. 49, Chapter 3, 1978, pp. 34-49 and 59-123. |
| Hoel P.G., “Elementary Statistics,” Fourth Edition, John Wiley & Sons, Inc., 1976, pp. 113-114. |
| Houghton Mifflin Company, “American Heritage Dictionary,” 4th Edition, 2000, pp. 82. |
| Houghton Mifflin Company, “Xenogenic, the American Heritage Stedman's Medical Dictionary,” 2002, Answers.Com, retrieved from http://www.answers.com/topic/xenogenic, on Nov. 7, 2006, 2 Pages. |
| Hrapovic S., et al., “Picoamperometric Detection of Glucose at Ultrasmall Platinum-Based Biosensors Preparation and Characterization,” Anal. Chem, vol. 75, 2003, pp. 3308-3315. |
| Hu Y., et al., “A Needle-Type Enzyme-Based Lactate Sensor for In Vivo Monitoring,” Analytica Chimica Acta, vol. 281, 1993, pp. 503-511. |
| Huang C., et al., “Electrochemical Generation of Oxygen. 1: The Effects of Anions and Cations on Hydrogen Chemisorption and Aniodic Oxide Film Formation on Platinum Electrode. 2: The Effects of Anions and Cations on Oxygen Generation on Platinum Electrode,” U.S. Department of Commence/NTIS, 1975, 126 pages. |
| Huang Q., et al., “A 0.5mW Passive Telemetry IC for Biomedical Applications,” Proceedings of the 23rd European Solid-State Circuits Conference (ESSCIRC '97), Southampton, UK, Sep. 16-18, 1997, pp. 172-175. |
| Hunter I., et al., “Minimally Invasive Glucose Sensor and Insulin Delivery System,” MIT Home Automation and Healthcare Consortium, Mar. 31, 2000, Progress Report No. 25, 17 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2006/034284 dated Sep. 9, 2008, 6 pages. |
| International Preliminary Report on Patentability for Application No. PCT/US2008/066600 dated Dec. 17, 2009, 6 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2006/032284, dated Jan. 18, 2007, 8 pages. |
| International Search Report and Written Opinion for Application No. PCT/US2008/066600 dated Oct. 7, 2008, 6 pages. |
| Ishikawa M., et al., “Initial Evaluation of A 290-Mm Diameter Subcutaneous Glucose Sensor: Glucose Monitoring With A Biocompatible, Flexible-Wire, Enzyme-Based Amperometric Microsensor in Diabetic and Nondiabetic Humans,” Journal of Diabetes and Its Complications, vol. 12, 1998, pp. 295-301. |
| Jablecki M., et al., “Simulations of the Frequency Response of Implantable Glucose Sensors,” Analytical Chemistry, vol. 72, 2000, 1853-1859. |
| Jaffari S.A., et al., “Recent Advances in Amperometric Glucose Biosensors for In Vivo Monitoring,” Physiological Measurement, 1995, vol. 16, pp. 1-15. |
| Jaremko J., et al., “Advances Toward the Implantable Artificial Pancreas for Treatment of Diabetes,” Diabetes Care, vol. 21 (3), Mar. 1998, pp. 444-450. |
| Jensen M.B., et al., “Fast Wave Forms for Pulsed Electrochemical Detection of Glucose by Incorporation of Reductive Desorption of Oxidation Products, ”Analytical Chemistry, vol. 69 (9), May 1997, pp. 1776-1781. |
| Jeong R.A., et al., “In Vivo Calibration of the Subcutaneous Amperometric Glucose Sensors Using a Non-Enzyme Electrode,” Biosensors and Bioelectronics, Elsevier, vol. 19, 2003, pp. 313-319. |
| Jeutter D.C., “A Transcutaneous Implanted Battery Recharging and Biotelemeter Power Switching System,” IEEE Transactions on Biomedical Engineering, vol. BME-29 (5), May 1982, pp. 314-321. |
| Jeutter D.C., et al., “Design of a Radio-Linked Implantable Cochlear Prosthesis Using Surface Acoustic Wave Devices,” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 40 (5), Sep. 1993, pp. 469-477. |
| Jobst G., et al., “Thin-Film Microbiosensors for Glucose-Lactate Monitoring,” Anal Chem, Sep. 15, 1996, vol. 68(18), pp. 3173-3179. |
| Johnson K.W., et al., “In Vivo Evaluation of an Electroenzymatic Glucose Sensor Implanted in Subcutaneous Tissue,” Biosensors and Bioelectronics, 1992, vol. 7, pp. 709-714. |
| Johnson K.W., “Reproducible Electrodeposition of Biomolecules for the Fabrication of Miniature Electroenzymatic Biosensors,” Sensors and Actuators B, vol. 5, 1991, pp. 85-89. |
| Joung G.B., et al., “An Energy Transmission System for an Artificial Heart Using Leakage Inductance Compensation of Transcutaneous Transformer,” IEEE Transactions on Power Electronics, vol. 13 (6), Nov. 1998, pp. 1013-1022. |
| Jovanovic L.M.D., “The Role of Continuous Glucose Monitoring in Gestational Diabetes Mellitus,” Diabetes Technology and Therapeutics, vol. 2 (1), 2000, pp. S67-S71. |
| Kacaniklic V., et al., “Amperometric Biosensors for Detection of L- and D-Amino Acids Based on Coimmoblized Peroxidase and L- and D-Amino Acid Oxidases in Carbon Paste Electrodes,” Electroanalysis, vol. 6, May-Jun. 1994, pp. 381-390. |
| Kamath A., et al., “Calibration of a Continuous Glucose Monitor: Effect of Glucose Rate of Change,” Eighth Annual Diabetes Technology Meeting, Nov. 13-15, 2008, pp. A88. |
| Kang S.K., et al., “In Vitro and Short-Term in Vivo Characteristics of a Kel-F Thin Film Modified Glucose Sensor,” Analytical Sciences, vol. 19, Nov. 2003, pp. 1481-1486. |
| Kaplan S.M., “Wiley Electrical and Electronics Engineering Dictionary,” IEEE Press, John Wiley & Sons, Inc., 2004, pp. 141, 142, 548 & 549. |
| Kargol M., et al., “Studies on the Structural Properties of Porous Membranes: Measurement of Linear Dimensions of Solutes,” Biophysical Chemistry, 2001, vol. 91, pp. 263-271. |
| Kaufman F.R., et al., “A Pilot Study of the Continuous Glucose Monitoring System,” Diabetes Care, vol. 24 (12), Dec. 2001, pp. 2030-2034. |
| Kaufman F.R., “Role of the Continuous Glucose Monitoring System in Pediatric Patients,” Diabetes Technology and Therapeutics, vol. 2 (1), 2000, S49-S52. |
| Kawagoe J.L., et al., “Enzyme-Modified Organic Conducting Salt Microelectrode,” Analytical Chemistry, vol. 63, 1991, pp. 2961-2965. |
| Keedy F.H., et al., “Determination of Urate in Undiluted Whole Blood by Enzyme Electrode,” Biosensors and Bioelectronics, vol. 6, 1991, pp. 491-499. |
| Kerner, et al., “A Potentially Implantable Enzyme Electrode for Amperometric Measurement of Glucose,” Hormone and Metabolic Research Supplement, vol. 20, 1988, pp. 8-13. |
| Kerner W., et al., “The Function of a Hydrogen Peroxide-Detecting Electroenzymatic Glucose Electrode is Markedly Impaired in Human Sub-Cutaneous Tissue and Plasma,” Biosensors and Bioelectronics, vol. 8, 1993, pp. 473-482. |
| Kerner W., “Implantable Glucose Sensors: Present Status and Future Developments,” Experimental and Clinical Endocrinol Diabetes, vol. 109 (2), 2001, pp. S341-S346. |
| Klonoff D., et al., “Performance Metrics for Continuous Interstitial Glucose Monitoring; Approved Guideline,” Clinical and Laboratory Standards Institute, POCT05-A, vol. 28 (33), 2008, 72 pages. |
| Klueh U., et al., “Use of Vascular Endothelial Cell Growth Factor Gene Transfer to Enhance Implantable Sensor Function in Vivo,” Biosensor Function and VEGF-Gene Transfer, vol. 67 (4), 2003, pp. 1072-1086. |
| Kondo T., et al., “A Miniature Glucose Sensor, Implantable in the Blood Stream,” Diabetes Care, vol. 5 (3), May-Jun. 1982, 218-221. |
| Koschinsky T., et al., “Sensors For Glucose Monitoring: Technical and Clinical Aspects,” Diabetes Metabolism Research and Reviews, vol. 17, 2001, pp. 113-123. |
| Koschinsky T., et al., “New Approach to Technical and Clinical Evaluation of Devices for Self-Monitoring of Blood Glucose,” Diabetes Care, vol. 11 (8), Sep. 1988, pp. 619-629. |
| Kost J., et al., “Glucose-Sensitive Membranes Containing Glucose Oxidase: Activity, Swelling, and Permeability Studies,” Journal of Biomedical Materials Research, vol. 19, 1985, pp. 1117-1133. |
| Koudelka M., et al., “In Vivo Response of Microfabricated Glucose Sensors to Glycemia Changes in Normal Rats,” Biomed. Biochim. Acta, vol. 48 (11/12), Nov.-Dec. 1989, pp. 953-956. |
| Koudelka M., et al., “In-Vivo Behaviour of Hypodermically Implanted Microfabricated Glucose Sensors,” Biosensors and Bioelectronics, vol. 6, 1991, pp. 31-36. |
| Kovatchev B.P., et al., “Evaluating the Accuracy of Continuous Glucose-Monitoring Sensors: Continuous Glucose-Error Grid Analysis Illustrated by TheraSense Freestyle Navigator Data,” Diabetes Care, vol. 27 (8), Aug. 2004, pp. 1922-1928. |
| KRAVER., et al., “A Mixed-Signal Sensor Interface Microinstrument,” Sensors and Actuators A, Physical 2001, vol. 91, pp. 266-277. |
| Krouwer J.S., “Setting Performance Goals and Evaluating Total Analytical Error for Diagnostic Assays,” Clinical Chemistry, vol. 48 (6), 2002, pp. 919-927. |
| Kruger D., et al., “Psychological Motivation and Patient Education: A Role for Continuous Glucose Monitoring,” Diabetes Technology and Therapeutics, vol. 2 (1), 2000, pp. S93-S97. |
| Kulys J., et al., “Carbon-Paste Biosensors Array for Long-Term Glucose Measurement,” Biosensors & Bioelectronics, vol. 9, 1994, pp. 491-500. |
| Kunjan K., et al., “Automated Blood Sampling and Glucose Sensing in Critical Care Settings,” Journal of Diabetes Science and Technology, vol. 2 (2), Mar. 2008, pp. 194-200. |
| Kurnik R.T., et al., “Application of the Mixtures of Experts Algorithm for Signal Processing in a Noninvasive Glucose Monitoring System,” Sensors and Actuators B, vol. 60, 1999, pp. 19-26. |
| Kurtz T.W., et al., “Recommendations for Blood Pressure Measurement in Humans and Experimental Animals, Part 2: Blood Pressure Measurement In Experimental Animals: A Statement for Professionals From the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research,” Hypertension, Feb. 2005, vol. 45, pp. 299-310. |
| Lacourse W.R., et al., “Optimization of Waveforms for Pulsed Amperometric Detection of Carbohydrates Based on Pulsed Voltammetry,” Analytical Chemistry, vol. 65, 1993, pp. 50-52. |
| Ladd M.F.C., et al., “Structure Determination By X-Ray Crystallography,” 3rd Edition, Plenum Press, 1994, Ch. 1, pp. xxi-xxiv and 1-58. |
| Lehmann E.D., et al., Retrospective Validation of a Physiological Model of Glucose-Insulin Interaction in Type 1 Diabetes Mellitus. Medical Engineering & Physics, vol. 16, May 1994, pp. 193-202. |
| Lerner., et al., “An Implantable Electrochemical Glucose Sensor,” Ann. N. Y. Acad. Sci., vol. 428, May 1984, pp. 263-278. |
| Lewandowski J.J., et al., “Evaluation of a Miniature Blood Glucose Sensor,” Transactions—American Society for Artificial Internal Organs, vol. 34, 1988, pp. 255-258. |
| Leypoldt J.K., et al., “Model of a Two-Substrate Enzyme Electrode for Glucose,” Analytical Chemistry, vol. 56, 1984, pp. 2896-2904. |
| Linke B., et al., “Amperometric Biosensor for In Vivo Glucose Sensing Based on Glucose Oxidase Immobilized In A Redox Hydrogel,” Biosensors and Bioelectronics, vol. 9, 1994, pp. 151-158. |
| Lohn A., et al., “A Knowledge-Based System for Real-Time Validation of Calibrations and Measurements,” Chemometrics and Intelligent Laboratory Systems, vol. 46, 1999, pp. 57-66. |
| Lowe C.R., “Biosensors,” Trends in Biotechnology, vol. 2 (3), 1984, pp. 59-65. |
| Luong J.H.T., et al., “Solubilization of Multiwall Carbon Nanotubes by 3-Aminopropyltriethoxysilane towards the Fabrication of Electrochemical Biosensors with Promoted Electron Transfer,” Electroanalysis, vol. 16 (1-2), 2004, pp. 132-139. |
| Lyandres O., et al. “Progress toward an In Vivo Surface-Enhanced Raman Spectroscopy Glucose Sensor,” Diabetes Technology and Therapeutics, vol. 10 (4), 2008, pp. 257-265. |
| Lynch S.M., et al., “Estimation-Based Model Predictive Control of Blood Glucose in Type I Diabetics: A Simulation Study,” Proceedings of the IEEE 27th Annual Northeast Bioengineering Conference, 2001, pp. 79-80. |
| Lynn P.A., “Recursive Digital Filters for Biological Signals,” Med. & Biol. Engineering, vol. 9, 1971, pp. 37-43. |
| Maidan R., et al., “Elimination of Electrooxidizable Interferent-Produced Currents in Amperometric Biosensors,” Analytical Chemistry, vol. 64, 1992, pp. 2889-2896. |
| Makale M.T., et al., “Tissue Window Chamber System for Validation of Implanted Oxygen Sensors,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 284, Feb. 21, 2003, pp. 1-27. |
| Malin S.F., et al., “Noninvasive Prediction of Glucose by Near-Infrared Diffuse Reflectance Spectroscopy,” Clinical Chemistry, vol. 45 (9), 1999, pp. 1651-1658. |
| Mancy K.H., et al., “A Galvanic Cell Oxygen Analyzer,” Journal of Electroanalytical Chemistry, vol. 4, 1962, pp. 65-92. |
| Maran A., et al., “Continuous Subcutaneous Glucose Monitoring in Diabetic Patients,” A Multicenter Analysis, Diabetes Care, vol. 25 (2), Feb. 2002, pp. 347-352. |
| March W.F., “Dealing with the Delay,” Diabetes Technology & Therapeutics, vol. 4 (1), 2002, pp. 49-50. |
| Marena S., et al., “The Artificial Endocrine Pancreas in Clinical Practice and Research,” Panminerva Medica, vol. 35 (2), 1993, pp. 67-74. |
| Martin R.F., “General Deming Regression for Estimating Systematic Bias and its Confidence Interval in Method-Comparison Studies,” Clinical Chemistry, vol. 46 (1), 2000, pp. 100-104. |
| Mascini M., et al., “Glucose Electrochemical Probe with Extended Linearity for Whole Blood,” Journal Pharmaceutical and Biomedical Analysis, vol. 7 (12), 1989, pp. 1507-1512. |
| Mastrototaro J.J., et al., “An Electroenzymatic Glucose Sensor Fabricated on a Flexible Substrate,” Sensors and Actuators B, vol. 5, 1991, pp. 139-144. |
| Mastrototaro J.J., et al., “Reproducibility of the Continuous Glucose Monitoring System Matches Previous Reports and the Intended Use of the Product,” Diabetes Care, vol. 26 (1), Jan. 2003, pp. 256-257. |
| Mastrototaro J.J., “The MiniMed Continuous Glucose Monitoring System,” Diabetes Technology & Therapeutics, vol. 2, Supplement 1, 2000, pp. S13-S18. |
| Matsuki H., “Energy Transfer System Utilizing Amorphous Wires For Implantable Medical Devices,” IEEE Transactions on Magnetics, vol. 31 (2), 1994, pp. 1276-1282. |
| Matsumoto T., et al., “A Micro-Planar Amperometric Glucose Sensor Unsusceptible to Interference Species,” Sensors and Actuators B, 49, 1998, pp. 68-72. |
| Matthews D.R., et al., “An Amperometric Needle-Type Glucose Sensor Testing in Rats and Man,” Diabetic Medicine, vol. 5, 1988, pp. 248-252. |
| Mazze R.S., et al., “Characterizing Glucose Exposure for Individuals with Normal Glucose Tolerance Using Continuous Glucose Monitoring and Ambulatory Glucose Profile Analysis,” Diabetes Technology & Therapeutics, vol. 10 (3), 2008, pp. 149-159. |
| Mazzola F., et al., “Video Diabetes: A Teaching Tool for Children with Insulin-Dependent Diabetes,” IEEE, Proceedings 7th Annual Symposium on Computer Applications in Medical Care, Oct. 1983, 1 page Abstract. |
| McCartney L.J., et al., “Near-Infrared Fluorescence Lifetime Assay for Serum Glucose Based on Allophycocyanin-Labeled Concanavalin A,” Analytical Biochemistry, vol. 292, 2001, pp. 216-221. |
| McGrath M.J., et al., “The Use of Differential Measurements with a Glucose Biosensor for Interference Compensation During Glucose Determinations by Flow Injection Analysis,” Biosens Bioelectron, vol. 10, 1995, pp. 937-943. |
| McKean B.D., et al., “A Telemetry Instrumentation System for Chronically Implanted Glucose and Oxygen Sensors,” IEEE Transactions on Biomedical Engineering, vol. 35 (7), Jul. 1988, pp. 526-532. |
| Memoli A., et al., “A Comparison between Different Immobilised Glucoseoxidase-Based Electrodes,” Journal of Pharmaceutical and Biomedical Analysis, vol. 29, 2002, pp. 1045-1052. |
| Merriam Webster Online Dictionary, Definition for “Aberrant,” retrieved from https://www.merriam-webster.com/dictionary/aberrant, Aug. 19, 2008, 1 page. |
| Merriam-Webster Online Dictionary, Definition of “Acceleration” retrieved from http://www.merriam-webster.com/dictionary/Acceleration, Jan. 11, 2010, 1 page. |
| Merriam-Webster Online Dictionary, Definition of “Nominal” retrieved from http://www.merriam-webster.com/dictionary/nominal, Apr. 23, 2007, 1 page. |
| Merriam-Webster Online Dictionary, Definition of “System”. http://www.merriamwebster.com/dictionary/System, Jan. 11, 2010, 2 pages. |
| Metzger M., et al., “Reproducibility of Glucose Measurements using the Glucose Sensor,” Diabetes Care, vol. 25 (6), Jul. 2002, pp. 1185-1191. |
| Meyerhoff C., et al., “On Line Continuous Monitoring of Subcutaneous Tissue Glucose in Men by Combining Portable Glucosensor With Microdialysis,” Diabetologia, vol. 35 (11), 1992, pp. 1087-1092. |
| Miller J.A., et al., “Development of an Autotuned Transcutaneous Energy Transfer System,” ASAIO Journal, vol. 39, 1993, pp. M706-M710. |
| Moatti-Sirat D., et al., “Evaluating In Vitro and In Vivo the Interference of Ascorbate and Acetaminophen on Glucose Detection by a Needle-Type Glucose Sensor,” Biosensors and Bioelectronics, vol. 7, 1992, pp. 345-352. |
| Moatti-Sirat D., et al., “Reduction of Acetaminophen Interference in Glucose Sensors by a Composite Nafion Membrane: Demonstration in Rats and Man,” Diabetologia, vol. 37 (6), Jun. 1994, pp. 610-616. |
| Moatti-Sirat., et al., “Towards Continuous Glucose Monitoring: In Vivo Evaluation of a Miniaturized Glucose Sensor Implanted for Several Days in Rat Subcutaneous Tissue,” Diabetologia, vol. 35, 1992, pp. 224-230. |
| Monsod T.P., et al., “Do Sensor Glucose Levels Accurately Predict Plasma Glucose Concentrations During Hypoglycemia and Hyperinsulinemia?,” Diabetes Care, vol. 25 (5), 2002, pp. 889-893. |
| Morff R.J., et al., “Microfabrication of Reproducible, Economical, Electroenzymatic Glucose Sensors,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 12 (2), 1990, pp. 0483-0484. |
| Mosbach K., et al., “Determination of Heat Changes in the Proximity of Immobilized Enzymes with an Enzyme Thermistor and its Use for the Assay of Metabolites,” Biochimica Biophysica Acta, vol. 403, 1975, pp. 256-265. |
| Motonaka J., et al., “Determination of Cholesterol and Cholesterol Ester with Novel enzyme Microsensors,” Anal. Chem., vol. 65, 1993, pp. 3258-3261. |
| Moussy F., et al., “A Miniaturized Nafion-Based Glucose Sensor: In Vitro and In Vivo Evaluation in Dogs,” International Journals of Artificial Organs, vol. 17 (2), 1994, pp. 88-94. |
| Moussy F., et al., “Performance of Subcutaneously Implanted Needle-Type Glucose Sensors Employing a Novel Trilayer Coating,” Analytical Chemistry, vol. 65, Aug. 1, 1993, pp. 2072-2077. |
| Moussy F., “Implantable Glucose Sensor: Progress and Problems,” IEEE, Nov. 2002, pp. 270-273. |
| Murphy S.M., et al., “Polymer Membranes in Clinical Sensor Applications, II. The Design and Fabrication of Permselective Hydrogels for Electrochemical Devices,” Biomaterials, 1992, vol. 13(14), pp. 979-990. |
| Muslu, “Trickling Filter Performance,” Applied Biochemistry and Biotechnology, vol. 37, 1992, pp. 211-224. |
| Myler S., et al., “Ultra-Thin-Polysiloxane-Film-Composite Membranes for the Optimisation of Amperometric Oxidase Enzyme Electrodes,” Biosensors & Bioelectronics, vol. 17, 2002, pp. 35-43. |
| Neuburger G.G., et al., “Pulsed Amperometric Detection of Carbohydrates at Gold Electrodes with a Two-Step Potential Waveform,” Anal. Chem., vol. 59, 1987, pp. 150-154. |
| Nintendo Healthcare, Wired, Dec. 1993, 1 page. |
| Novo Nordisk Pharmaceuticals Inc., “Diabetes Educational Video Game Recognized by Software Publishers Association,” Press Release, Mar. 14, 1994, 4 pages. |
| Office Action for U.S. Appl. No. 09/636,369, dated Sep. 30, 2002, 4 pages. |
| Office Action for U.S. Appl. No. 10/991,966, dated Jul. 22, 2008, 12 pages. |
| Office Action for U.S. Appl. No. 10/991,966, dated Nov. 28, 2007, 13 pages. |
| Office Action for U.S. Appl. No. 11/038,340, dated Jan. 5, 2009, 13 pages. |
| Office Action for U.S. Appl. No. 11/038,340, dated Jun. 17, 2008, 11 pages. |
| Office Action for U.S. Appl. No. 11/038,340, dated May 19, 2009, 14 pages. |
| Office Action for U.S. Appl. No. 11/038,340, dated Nov. 9, 2009, 16 pages. |
| Office Action for U.S. Appl. No. 11/077,759, dated Jul. 10, 2008, 10 pages. |
| Office Action for U.S. Appl. No. 11/077,759, dated Mar. 31, 2008, 16 pages. |
| Office Action for U.S. Appl. No. 11/077,759, dated May 26, 2009, 8 pages. |
| Office Action for U.S. Appl. No. 11/334,876, dated Aug. 25, 2009, 18 pages. |
| Office Action for U.S. Appl. No. 11/334,876, dated Aug. 26, 2008, 8 pages. |
| Office Action for U.S. Appl. No. 11/334,876, dated May 2, 2008, 18 pages. |
| Office Action for U.S. Appl. No. 11/334,876, dated Oct. 4, 2006, 9 pages. |
| Office Action for U.S. Appl. No. 11/334,876, dated Sep. 25, 2007, 14 pages. |
| Office Action for U.S. Appl. No. 11/360,252, dated Jan. 29, 2009, 15 pages. |
| Office Action for U.S. Appl. No. 11/360,252, dated Jul. 23, 2009, 10 pages. |
| Office Action for U.S. Appl. No. 11/360,252, dated Jun. 30, 2008, 10 pages. |
| Office Action for U.S. Appl. No. 11/360,819, dated Apr. 7, 2010, 10 pages. |
| Office Action for U.S. Appl. No. 11/360,819, dated Aug. 11, 2008, 10 pages. |
| Office Action for U.S. Appl. No. 11/360,819, dated Dec. 26, 2008, 12 pages. |
| Office Action for U.S. Appl. No. 11/360,819, dated Oct. 29, 2009, 15 pages. |
| Office Action for U.S. Appl. No. 12/102,654, dated Jul. 30, 2009, 9 pages. |
| Office Action for U.S. Appl. No. 12/102,654, dated Mar. 10, 2010, 6 pages. |
| Office Action for U.S. Appl. No. 12/102,745, dated Dec. 23, 2008, 4 pages. |
| Office Action for U.S. Appl. No. 95/001,038, dated Jun. 17, 2008, 32 pages. |
| Office Action for U.S. Appl. No. 95/001,039, dated May 29, 2008, 21 pages. |
| Ohara T.J., et al., “Glucose Electrodes Based On Cross-Linked [Os(bpy)2Cl](+/2+) Complexed Poly(1-Vinylimidazole) Films,” Analytical Chemistry, vol. 65, Dec. 1993, pp. 3512-3517. |
| Ohara T.J., et al., ““Wired” Enzyme Electrodes for Amperometric Determination of Glucose or Lactate in the Presence of Interfering Substances,” Anal Chem, vol. 66, 1994, pp. 2451-2457. |
| Okuda, et al., “Mutarotase Effect on Micro Determinations of D-Glucose and its Anomers with β D-Glucose Oxidase,” Anal Biochem, vol. 43 (1), 1971, pp. 312-315. |
| Oxford English Dictionary Online, Definition of “Impending,” http://www.askoxford.com/results/?view=devdict&field-12668446_Impending&branch=, Jan. 11, 2010, 1 page. |
| Palmisano F., et al., “Simultaneous Monitoring of Glucose and Lactate by an Interference and Cross-Talk Free Dual Electrode Amperometric Biosensor Based on Electropolymerized Thin Films,” Biosensors & Bioelectronics, vol. 15, 2000, pp. 531-539. |
| Panteleon A.E., et al., “The Role of the Independent Variable to Glucose Sensor Calibration,” Diabetes Technology & Therapeutics, vol. 5 (3), 2003, pp. 401-410. |
| Parker R.S., et al., “A Model-Based Algorithm for Blood Glucose Control In Type I Diabetic Patients,” IEEE Trans Biomed Engg (BME), vol. 46(2), 1999, pp. 148-157. |
| Patel H., et al., “Amperometric Glucose Sensors Based on Ferrocene Containing Polymeric Electron Transfer Systems—A Preliminary Report,” Biosensors & Bioelectronics, vol. 18, 2003, pp. 1073-1076. |
| Peacock W.F., et al., “Cardiac Troponin and Outcome in Acute Heart Failure,” N. Engl. J. Med., vol. 358, 2008, pp. 2117-2126. |
| Pfeiffer E.F., et al., “On Line Continuous Monitoring of Subcutaneous Tissue Glucose is Feasible by Combining Portable Glucosensor with Microdialysis,” Horm. Metab. Res., vol. 25, 1993, pp. 121-124. |
| Pfeiffer E.F., “The Glucose Sensor: The Missing Link in Diabetes Therapy,” Horm Metab Res Suppl., vol. 24, 1990, pp. 154-164. |
| Phillips R.P., “A High Capacity Transcutaneous Energy Transmission System,” ASIAO Journal, vol. 41, 1995, pp. M259-M262. |
| Pichert J.W., et al., “Issues for the Coming Age of Continuous Glucose Monitoring,” Diabetes Educator, vol. 26 (6), Nov.-Dec. 2000, pp. 969-980. |
| Pickup J.C., et al., “Developing Glucose Sensors for In Vivo Use,” Elsevier Science Publishers Ltd (Uk), TIBTECH, vol. 11, 1993, pp. 285-291. |
| Pickup J.C., et al., “Implantable Glucose Sensors: Choosing the Appropriate Sensor Strategy,” Biosensors, vol. 3, (1987/1988), pp. 335-346. |
| Pickup J.C., et al., “In Vivo Molecular Sensing in Diabetes Mellitus: An Implantable Glucose Sensor with Direct Electron Transfer,” Diabetologia, vol. 32, 1989, pp. 213-217. |
| Pickup J.C., et al., “Potentially-Implantable, Amperometric Glucose Sensors with Mediated Electron Transfer: Improving the Operating Stability,” Biosensors, vol. 4, 1989, pp. 109-119. |
| Pickup J.C., et al., “Responses and Calibration of Amperometric Glucose Sensors Implanted in the Subcutaneous Tissue of Man,” ACTA Diabetol, vol. 30, 1993, pp. 143-148. |
| Pinner S.H., et al., “Cross-Linking of Cellulose Acetate by Ionizing Radiation,” Nature, vol. 184, Oct. 24, 1959, pp. 1303-1304. |
| Pishko M.V., et al., “Amperometric Glucose Microelectrodes Prepared Through Immobilization of Glucose Oxidase in Redox Hydrogels,” Analytical Chemistry, vol. 63 (20), 1991, pp. 2268-2272. |
| Pitzer K.R., et al., “Detection of Hypogylcemia with the Glucowatch Biographer,” Diabetes Care, vol. 24 (5), 2001, pp. 881-885. |
| Poirier J.Y., et al., “Clinical and Statistical Evaluation of Self-Monitoring Blood Glucose Meters,” Diabetes Care, vol. 21 (11), Nov. 1998, pp. 1919-1924. |
| Poitout V., et al., “A Glucose Monitoring System for on Line Estimation in Man of Blood Glucose Concentration Using a Miniaturized Glucose Sensor Implanted in the Subcutaneous Tissue and a Wearable Control Unit,” Diabetologia, vol. 36, 1993, pp. 658-663. |
| Poitout V., et al., “Development of a Glucose Sensor for Glucose Monitoring in Man: The Disposable Implant Concept,” Clinical Materials, vol. 15, 1994, pp. 241-246. |
| Poitout V., et al., “In Vitro and In Vivo Evaluation in Dogs of a Miniaturized Glucose Sensor,” ASAIO Transactions, vol. 37, 1991, pp. M298-M300. |
| Postlethwaite T.A., et al., “Interdigitated Array Electrode as an Alternative to the Rotated Ring-Disk Electrode for Determination of the Reaction Products of Dioxygen Reduction,” Analytical Chemistry, vol. 68 (17), Sep. 1996, pp. 2951-2958. |
| Prabhu V.G., et al., “Electrochemical Studies of Hydrogen Peroxide at a Platinum Disc Electrode,” Electrochimica Acta, vol. 26 (6), 1981, pp. 725-729. |
| Quinn C.A.P., et al., “Biocompatible, Glucose-Permeable Hydrogel for In situ Coating of Implantable Biosensors,” Biomaterials, vol. 18 (24), 1997, pp. 1665-1670. |
| Quinn C.P., et al., “Kinetics of Glucose Delivery to Subcutaneous Tissue in Rats Measured with 0.3-mm Amperometric Microsensors,” The American Physiological Society, vol. 269, 1995, pp. E155-E161. |
| Rabah M.A., et al., “Electrochemical Wear of Graphite Anodes During Electrolysis of Brine,” Carbon, vol. 29 (2), 1991, pp. 165-171. |
| Raya Systems Pioneers, “Raya Systems Pioneers Healthy Video Games,” PlayRight, Nov. 1993, pp. 14-15. |
| Reach G., “A Method for Evaluating in vivo the Functional Characteristics of Glucose Sensors,” Biosensors, vol. 2, 1986, pp. 211-220. |
| Reach G., et al., “Can Continuous Glucose Monitoring Be Used for the Treatment of Diabetes?,” Analytical Chemistry, vol. 64 (6), Mar. 15, 1992, pp. 381A-386A. |
| Reach G., “Which Threshold to Detect Hypoglycemia? Value of Receiver-Operator Curve Analysis to Find a Compromise Between Sensitivity and Specificity,” Diabetes Care, vol. 24 (5), May 2001, pp. 803-804. |
| Rebrin K., et al., “Automated Feedback Control of Subcutaneous Glucose Concentration in Diabetic Dogs,” Diabetologia, vol. 32, 1989, pp. 573-576. |
| Rebrin K., et al., “Subcutaenous Glucose Monitoring by Means of Electrochemical Sensors: Fiction or Reality?,” Journal of Biomedical Engineering, vol. 14, Jan. 1992, pp. 33-40. |
| Rebrin K., et al., “Subcutaneous Glucose Predicts Plasma Glucose Independent of Insulin: Implications for Continuous Monitoring,” The American Physiological Society, vol. 277, 1999, pp. E561-E571. |
| Reush, “Organometallic Compounds,” Chemical Reactivity, Virtual Textbook of Organic Chemistry, Retrieved from http://www.cem.msu.edu/-reuschlVirtualText/orgmetal.htm, 2004, pp. 1-16. |
| Rhodes R.K., et al., “Prediction of Pocket-Portable and Implantable Glucose Enzyme Electrode Performance from Combined Species Permeability and Digital Simulation Analysis,” Analytical Chemistry, vol. 66 (9), May 1, 1994, pp. 1520-1529. |
| Rigla M., et al., “Real-Time Continuous Glucose Monitoring Together with Telemedical Assistance Improves Glycemic Control and Glucose Stability in Pump-Treated Patients,” Diabetes Technology & Therapeutics, vol. 10 (3), 2008, pp. 194-199. |
| Rinken T., et al., “Calibration of Glucose Biosensors By Using Pre-Steady State Kinetic Data,” Biosensors & Bioelectronics, vol. 13, 1998, pp. 801-807. |
| Rivers E.P., et al., “Central Venous Oxygen Saturation Monitoring in the Critically III Patient,” Current Opinion in Critical Care, 2001, vol. 7, pp. 204-211. |
| Sakakida M., et al., “Development of Ferrocene-Mediated Needle-Type Glucose Sensor as a Measure of True Subcutaneous Tissue Glucose Concentrations,” Artif. Organs Today, vol. 2 (2), 1992, pp. 145-158. |
| Sakakida M., et al., “Ferrocene-Mediated Needle Type Glucose Sensor Covered with Newly Designed Biocompatible Membrane,” Sensors and Actuators B, vol. 13-14, 1993, pp. 319-322. |
| Salardi S., et al., “The Glucose Area Under the Profiles Obtained with Continuous Glucose Monitoring System Relationships with HbA1C in Pediatric Type 1 Diabetic Patients,” Diabetes Care, vol. 25 (10), Oct. 2002, pp. 1840-1844. |
| Samuels M.P., “The Effects of Flight and Altitude,” Arch Dis Child, vol. 89, 2004, pp. 448-455. |
| San Diego Plastics Inc, “Polyethylene,” Datasheet, Retrieved from http://www.sdplastics.com/polyeth.html on Aug. 19, 2009, 7 pages. |
| Sansen W., et al., “A Smart Sensor for the Voltammetric Measurement of Oxygen or Glucose Concentrations,” Sensors and Actuators B1, 1990, pp. 298-302. |
| Sansen W., et al., “Glucose Sensor with Telemetry System,” In Implantable Sensors for Closed Loop Prosthetic Systems edited by KO W.H, Chapter 12, 1985, pp. 167-175. |
| Schmidt F.J., et al., “Calibration of a Wearable Glucose Sensor,” The International Journal of Artificial Organs, Wichtig Publishing, IT, vol. 15(1), Jan. 1, 1992, pp. 55-61. |
| Schmidt F.J., et al., “Glucose Concentration in Subcutaneous Extracellular Space,” Diabetes Care, vol. 16 (5), May 1993, pp. 695-700. |
| Schmidtke D.W., et al., “Accuracy of the One-Point in Vivo Calibration of “Wired” Glucose Oxidase Electrodes Implanted in Jugular Veins of Rats in Periods of Rapid Rise and Decline of the Glucose Concentration,” Analytical Chemistry, vol. 70 (10), May 15, 1998, pp. 2149-2155. |
| Schmidtke D.W., et al., “Measurement and Modeling of the Transient Difference Between Blood and Subcutaneous Glucose Concentrations in the Rat After Injection of Insulin,” Proceedings of the National Academy of Sciences, vol. 95, Jan. 1998, pp. 294-299. |
| Schoemaker M., et al., “The SCGMI System: Subcutaneous Continuous Glucose Monitoring Based on Microdialysis Technique,” Diabetes Technology & Therapeutics, vol. 5 (4), 2003, pp. 599-608. |
| Schoonen A.J.M., et al., “Development of a Potentially Wearable Glucose Sensor for Patients with Diabetes Mellitus: Design and In-vitro Evaluation,” Biosensors & Bioelectronics, vol. 5, 1990, pp. 37-46. |
| Schuler, et al., “Modified Gas-Permeable Silicone Rubber Membranes for Covalent Immobilisation of Enzymes and their Use in Biosensor Development,” Analyst, 1999, vol. 124, pp. 1181-1184. |
| Service F.J., et al., “Mean Amplitude of Glycemic Excursions, A Measure of Diabetic Instability,” Diabetes, vol. 19 (9), Sep. 1970, pp. 644-655. |
| Service F.J., et al., “Measurements of Glucose Control,” Diabetes Care, vol. 10 (2), Mar.-Apr. 1987, pp. 225-237. |
| Service R.F., “Can Sensors Make a Home in the Body?,” Science, Materials Science: Soft Surface, vol. 297, Aug. 9, 2002, pp. 962-963. |
| Sharkawy A.A., et al., “Engineering the Tissue Which Encapsulates Subcutaneous Implants. I. Diffusion Properties,” Journal of Biomedical Materials Research, vol. 37, 1997, pp. 401-412. |
| Shaw G.W., et al., “In Vitro Testing of a Simply Constructed, Highly Stable Glucose Sensor Suitable for Implantation in Diabetic Patients,” Biosensors & Bioelectronics, vol. 6, 1991, pp. 401-406. |
| Shichiri M., et al., “Glycaemic Control in Pancreatectomized Dogs with a Wearable Artificial Endocrine Pancreas,” Diabetologia, vol. 24, 1983, pp. 179-184. |
| Shichiri M., et al., “Membrane Design for Extending the Long-Life of an Implantable Glucose Sensor,” Diabetes Nutrition & Metabolism, vol. 2 (4), 1989, pp. 309-313. |
| Shichiri M., et al., “Needle Type Glucose Sensor for Wearable Artificial Endocrine Pancreas,” In Implantable Sensors for Closed-Loop Prosthetic Systems edited by KO W.H, Chapter 15, 1985, pp. 197-210. |
| Shichiri M., et al., “Telemetry Glucose Monitoring Device with Needle-Type Glucose Sensor: A Useful Tool for Blood Glucose Monitoring in Diabetic Individuals,” Diabetes Care, vol. 9 (3), May-Jun. 1986, pp. 298-301. |
| Shichiri M., et al., “Wearable Artificial Endocrine Pancreas with Needle-Type Glucose Sensor,” Preliminary Communication, Lancet, vol. 2, Nov. 20, 1982, pp. 1129-1131. |
| Shults M.C., et al., “A Telemetry-Instrumentation System for Monitoring Multiple Subcutaneously Implanted Glucose Sensors,” IEEE Transactions on Biomedical Engineering, vol. 41 (10), Oct. 1994, pp. 937-942. |
| Sigma-Aldrich Corp “Nafion® 117 Solution Product Description, Product No. 70160,” retrieved from https//:www.sigmaaldrich.com/cgi-bin/hsrun/Suite7/Suite/HAHTpage/Suite.HsExternalProd on Apr. 7, 2005, 1 page. |
| Skyler J.S., “The Economic Burden of Diabetes and the Benefits of Improved Glycemic Control: The Potential Role of a Continuous Glucose Monitoring System,” Diabetes Technology & Therapeutics, vol. 2, Supplement 1,2000, pp. S7-S12. |
| Slater-MacLean L., et al., “Accuracy of Glycemic Measurements in the Critically Ill,” Diabetes Technology and Therapeutics, vol. 10 (3), 2008, pp. 169-177. |
| Smith B., et al., “An Externally Powered, Multichannel, Implantable Stimulator-Telemeter for Control of Paralyzed Muscle,” IEEE Transactions on Biomedical Engineering, vol. 45 (4), Apr. 1998, pp. 463-475. |
| Sokol L., et al., “Immobilized-Enzyme Rate-Determination Method for Glucose Analysis,” Clinical Chemistry, vol. 26 (1), 1980, pp. 89-92. |
| Sokolov S., et al., “Metrological Opportunities of the Dynamic Mode of Operating an Enzyme Amperometric Biosensor,” Medical Engineering & Physics, vol. 17 (6), 1995, pp. 471-476. |
| Sparacino G., et al., “Continuous Glucose Monitoring Time Series and Hypo-Hyperglycemia Prevention: Requirements, Methods, Open Problems,” Current Diabetes Reviews, vol. 4 (3), 2008, pp. 181-192. |
| Sproule B.A., et al., “Fuzzy Pharmacology: Theory and Applications,” Trends in Pharmacological Sciences, vol. 23 (9), Sep. 2002, pp. 412-417. |
| Sriyudthsak M., et al., “Enzyme-Epoxy Membrane Based Glucose Analyzing System and Medical Applications,” Biosensors & Bioelectronics, vol. 11 (8), 1996, pp. 735-742. |
| Steil G.M., et al., “Determination of Plasma Glucose During Rapid Glucose Excursions with a Subcutaneous Glucose Sensor,” Diabetes Technology & Therapeutics, vol. 5 (1), 2003, pp. 27-31. |
| Stern M., et al., “Electrochemical Polarization: I. A Theoretical Analysis of the Shape of Polarization Curves,” Journal of the Electrochemical Society, vol. 104 (1), Jan. 1957, pp. 56-63. |
| Sternberg F., et al., “Does Fall in Tissue Glucose Precede Fall in Blood Glucose?,” Diabetologia, vol. 39, 1996, pp. 609-612. |
| Sternberg R., et al., “Study and Development of Multilayer Needle-type Enzyme Based Glucose Microsensors,” Biosensors, vol. 4, 1988, pp. 27-40. |
| Street J.O., et al., “A Note on Computing Robust Regression Estimates Via Iteratively Reweighted Least Squares,” The American Statistician, vol. 42 (2), May 1988, pp. 152-154. |
| Sumino T., et al., “Preliminary Study of Continuous Glucose Monitoring with a Microdialysis Technique,” Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20 (4), 1998, pp. 1775-1778. |
| Takegami S., et al., “Pervaporation of Ethanol/Water Mixtures Using Novel Hydrophobic Membranes Containing Polydimethylsiloxane,” Journal of Membrane Science, vol. 75, 1992, pp. 93-105. |
| Tamura T., et al., “Preliminary Study of Continuous Glucose Monitoring with a Microdialysis Technique and a Null Method—A Numerical Analysis,” Frontiers of Medical & Biological Engineering, vol. 10 (2), 2000, pp. 147-156. |
| Tanenberg R.J., et al., “Continuous Glucose Monitoring System: A New Approach to the Diagnosis of Diabetic Gastroparesis,” Diabetes Technology & Therapeutics, vol. 2, Supplement 1,2000, pp. S73-S80. |
| Tatsuma T., et al., “Oxidase/Peroxidase Bilayer-Modified Electrodes as Sensors for Lactate, Pyruvate, Cholesterol and Uric Acid,” Analytica Chimica Acta, vol. 242, 1991, pp. 85-89. |
| Thijssen, et al., “A Kalman Filter for Calibration, Evaluation of Unknown Samples and Quality Control in Drifting Systems,” Part 1 ,Theory and Simulations, Analytica chimica Acta, 1984, vol. 156, pp. 87-101. |
| Thijssen, et al., “A Kalman Filter for Calibration, Evaluation of Unknown Samples and Quality Control in Drifting Systems,” Part 3, Variance Reduction ,Analytica chimica Acta, 1985, vol. 173, pp. 265-272. |
| Thijssen, et al., “A Kalman Filter for Calibration, Evaluation of Unknown Samples and Quality Control in Drifting Systems,” Part 4,Flow Injection Analysis, Analytica chimica Acta, 1985, vol. 174, pp. 27-40. |
| Thijssen P.C.,“A Kalman Filter for Calibration, Evaluation of Unknown Samples and Quality Control in Drifting Systems,” Part 2,Optimal Designs, Analytica chimica Acta, vol. 162, 1984, pp. 253-262. |
| Thome V., et al., “(Abstract) Can the Decrease in Subcutaneous Glucose Concentration Precede the Decrease in Blood Glucose Level? Proposition for a Push-Pull Kinetics Hypothesis,” Horm. metab. Res., vol. 27, 1995, p. 53. |
| Thome-Duret V., et al., “Continuous Glucose Monitoring in the Free-Moving Rat,” Metabolism, vol. 47 (7), Jul. 1998, pp. 799-803. |
| Thome-Duret V., et al., “Modification of the Sensitivity of Glucose Sensor Implanted into Subcutaneous Tissue,” Diabetes & Metabolism, vol. 22, 1996, pp. 174-178. |
| Thome-Duret V., et al., “Use of a Subcutaneous Glucose Sensor to Detect Decreases in Glucose Concentration Prior to Observation in Blood,” Analytical Chemistry, vol. 68 (21), Nov. 1, 1996, pp. 3822-3826. |
| Thompson M., et al., “In Vivo Probes: Problems and Perspectives,” Clinical Biochemistry, vol. 19 (5), Oct. 1986, pp. 255-261. |
| Tierney M.J., et al., “Effect of Acetaminophen on the Accuracy of Glucose Measurements Obtained with the GlucoWatch Biographer,” Diabetes Technology & Therapeutics, vol. 2 (2), 2000, pp. 199-207. |
| Tierney M.J., et al., “The Gluco Watch® Biographer: A Frequent, Automatic and Noninvasive Glucose Monitor,” Annals of Medicine, vol. 32, 2000, pp. 632-641. |
| Tilbury J.B., et al., “Receiver Operating Characteristic Analysis for Intelligent Medical Systems—A New Approach for Finding Confidence Intervals,” IEEE Transactions on Biomedical Engineering, vol. 47 (7), Jul. 2000, pp. 952-963. |
| Torjman M.C., et al., “Glucose Monitoring in Acute Care: Technologies on the Horizon,” Journal of Diabetes Science and Technology, vol. 2 (2), Mar. 2008, pp. 178-181. |
| Trajanoski Z., et al., “Neural Predictive Controller For Insulin Delivery Using The Subcutaneous Route,” IEEE Transactions on Biomedical Engineering, vol. 45(9), 1998, pp. 1122-1134. |
| Trecroci D., “A Glimpse into the Future-Continuous Monitoring of Glucose with a Microfiber,” Diabetes Interview, Jul. 2002, pp. 42-43. |
| TSE P.S.H., et al., “Time-Dependent Inactivation of Immobilized Glucose Oxidase and Catalase,” Biotechnology & Bioengineering, vol. 29, 1987, pp. 705-713. |
| Turner A.P.F., et al., “Carbon Monoxide: Acceptor Oxidoreductase from Pseudomonas Thermocarboxydovorans Strain C2 and its Use in a Carbon Monoxide Sensor,” Analytica Chimica Acta, vol. 163, 1984, pp. 161-174. |
| Turner A.P.F., et al., “Diabetes Mellitus: Biosensors for Research and Management,” Biosensors, vol. 1, 1985, pp. 85-115. |
| Unger J., et al., “Glucose Control in the Hospitalized Patient,” Emergency Medicine, vol. 36 (9), 2004, pp. 12-18. |
| Updike S.J., et al., “A Subcutaneous Glucose Sensor with Improved Longevity, Dynamic Range, and Stability of Calibration,” Diabetes Care, vol. 23 (2), Feb. 2000, pp. 208-214. |
| Updike S.J., et al., “Continuous Glucose Monitor Based on an Immobilized Enzyme Electrode Detector,” Journal of Laboratory and Clinical Medicine, vol. 93(4), 1979, pp. 518-527. |
| Updike S.J., et al., “Enzymatic Glucose Sensor: Improved Long-Term Performance in Vitro and In Vivo,” ASAIO Journal, vol. 40 (2), Apr.-Jun. 1994, pp. 157-163. |
| Updike S.J., et al., “Implanting the Glucose Enzyme Electrode: Problems, Progress, and Alternative Solutions,” Diabetes Care, vol. 5 (3), May-Jun. 1982, pp. 207-212. |
| Updike S.J., et al., “Laboratory Evaluation of New Reusable Blood Glucose Sensor,” Diabetes Care, vol. 11 (10), Nov.-Dec. 1988, pp. 801-807. |
| Updike S.J., et al., “Principles of Long-Term Fully Implanted Sensors with Emphasis on Radiotelemetric Monitoring of Blood Glucose Form Inside a Subcutaneous Foreign Body Capsule (FBC),” Edited by Fraser D M, Biosensors in the Body: Continuous in vivo Monitoring, John Wiley & Sons Ltd., New York, 1997, Chapter 4, pp. 117-137. |
| Updike S.J., et al., “The Enzyme Electrode,” Nature, vol. 214, Jun. 3, 1967, pp. 986-988. |
| Utah Medical Products Inc., “Deltran—Disposable Blood Pressure Tranducers,” Product Specifications, 2003-2006, 6 pages. |
| Vadgama P., “Diffusion Limited Enzyme Electrodes,” NATO ASI Series: Series C, Math and Phys. Sci, vol. 226, 1988, pp. 359-377. |
| Vadgama P., “Enzyme Electrodes as Practical Biosensors,” Journal of Medical Engineering & Technology, vol. 5 (6), Nov. 1981, pp. 293-298. |
| Valdes T.I., et al., “In Vitro and In Vivo Degradation of Glucose Oxidase Enzyme used for an Implantable Glucose Biosensor,” Diabetes Technology & Therapeutics, vol. 2 (3), 2000, pp. 367-376. |
| Van Den Berghe, “Tight Blood Glucose Control with Insulin in “Real-Life” Intensive Care,” Mayo Clinic Proceedings, vol. 79 (8), Aug. 2004, pp. 977-978. |
| Velho G., et al., “In Vitro and In Vivo Stability of Electrode Potentials in Needle-Type Glucose Sensors,” Influence of Needle Material, Diabetes, vol. 38, Feb. 1989, pp. 164-171. |
| Velho G., et al., “Strategies for Calibrating a Subcutaneous Glucose Sensor,” Biomed Biochim Acta, vol. 48 (11/12), 1989, pp. 957-964. |
| Von Woedtke T., et al., “In Situ Calibration of Implanted Electrochemical Glucose Sensors,” Biomed. Biochim. Acta 48 vol. 11/12, 1989, pp. 943-952. |
| Wagner, et al., “Continuous Amperometric Monitoring of Glucose in a Brittle Diabetic Chimpanzee with a Miniature Subcutaneous Electrode,” Proc. Natl. Acad. Sci. USA, vol. 95, May 1998, pp. 6379-6382. |
| Wang J., “Electrochemical Glucose Biosensors,” American Chemical Society, Chemical Reviews, Published on Web Dec. 23, 2007, pp. 1-12. |
| Wang J., et al., “Highly Selective Membrane-Free Mediator-Free Glucose Biosensor,” Analytical Chemistry, vol. 66 (21), Nov. 1, 1994, pp. 3600-3603. |
| Wang X., et al., “Improved Ruggedness for Membrane-Based Amperometric Sensors using a Pulsed Amperometric Method,” Analytical Chemistry, vol. 69 (21), Nov. 1, 1997, pp. 4482-4489. |
| Ward, et al., “A Wire-Based Dual-Analyte Sensor for Glucose and Lactate: In Vitro and In Vivo Evaluation,” Diabetes Technology and Therapeutics, 2004, vol. 6 (3), pp. 389-401. |
| Ward W.K., et al., “A New Amperometric Glucose Microsensor: In Vitro and Short-Term In Vivo Evaluation,” Biosensors & Bioelectronics, vol. 17, 2002, pp. 181-189. |
| Ward W.K., et al., “Assessment of Chronically Subcutaneous Glucose Sensors in Dogs: The Effect of Surrounding Fluid Masses,” ASAIO Journal, 1999, vol. 45 (6), pp. 555-561. |
| Ward W.K., et al., “Rise in Background Current Over Time in a Subcutaneous Glucose Sensor in the Rabbit,” Relevance to Calibration and Accuracy, Biosensors & Bioelectronics, vol. 15, 2000, pp. 53-61. |
| Ward W.K., et al., “Understanding Spontaneous Output Fluctuations of an Amperometric Glucose Sensor: Effect of Inhalation Anesthesia and Use of a Nonenzyme Containing Electrode,” ASAIO Journal, 2000, pp. 540-546. |
| Wientjes K.J.C., “Development of a Glucose Sensor for Diabetic Patients,” (Ph.D. Thesis), 2000, 212 pages. |
| Wikipedia., “Intravenous Therapy,” http://en.wikipedia.org/wiki/Intravenous_therapy, Aug. 15, 2006, 6 pages. |
| Wilkins E., et al., “Glucose Monitoring: State of the Art and Future Possibilities,” Med. Eng. Phys., vol. 18 (4), 1996, pp. 273-288. |
| Wilkins E., et al., “Integrated Implantable Device for Long-Term Glucose Monitoring,” Biosensors & Bioelectronics, vol. 10, 1995, pp. 485-494. |
| Wilkins E.S., et al., “The Coated Wire Electrode Glucose Sensor,” Horm Metab Res Suppl., vol. 20, 1988, pp. 50-55. |
| Wilson G.S., et al., “Enzyme-Based Biosensors for In Vivo Measurements,” Chem. Rev., vol. 100, 2000, pp. 2693-2704. |
| Wilson G.S., et al., “Progress Toward the Development of an Implantable Sensor for Glucose,” Clinical Chemistry, vol. 38 (9), 1992, pp. 1613-1617. |
| Wood W D., et al., “Hermetic Sealing with Epoxy,” Pave Technology-Mechanical Engineering, Mar. 1990, 3 pages. |
| Woodward S.C., “How Fibroblasts and Giant Cells Encapsulate Implants: Considerations in Design of Glucose Sensor,” Diabetes Care, vol. 5 (3) May-Jun. 1982, pp. 278-281. |
| Worsley G.J et al., “Measurement of Glucose in Blood with a Phenylboronic Acid Optical Sensor,” Journal of Diabetes Science and Technology, vol. 2 (2), Mar. 2008, pp. 213-220. |
| Wright M., et al., “Bioelectrochemical Dehalogenations Via Direct Electrochemistry of Poly(ethylene oxide)-Modified Myoglobin,” Electrochemistry Communications, vol. 1, 1999, pp. 609-613. |
| Wu H., et al., “In Situ Electrochemical Oxygen Generation with an Immunoisolation Device,” Annals New York Academy of Sciences, vol. 875, 1999, pp. 105-125. |
| Yamasaki Y., et al., “Direct Measurement of Whole Blood Glucose by a Needle-Type Sensor,” Clinica Chimica Acta. 93, 1989, pp. 93-98. |
| Yamasaki Y., “The Development of a Needle-Type Glucose Sensor for Wearable Artificial Endocrine Pancreas,” Medical Journal of Osaka University, vol. 35 (1-2), Sep. 1984, pp. 25-34. |
| Yang C., et al., “A Comparison of Physical Properties and Fuel Cell Performance of Nation and Zirconium Phosphate/Nation Composite Membranes,” Journal of Membrane Science, vol. 237, 2004, pp. 145-161. |
| Yang Q., et al., “Development of Needle-Type Glucose Sensor with High Selectivity,” Science and Actuators B, vol. 46, 1998, pp. 249-256. |
| Yang S., et al., “A Glucose Biosensor Based On an Oxygen Electrode: In-Vitro Performances in a Model Buffer Solution and in Blood Plasma,” Biomedical Instrumentation & Technology, vol. 30 (1), 1996, pp. 55-61. |
| Ye L., et al., “High Current Density Wired' Quinoprotein Glucose Dehydrogenase Electrode,” Analytical Chemistry, vol. 65, 1993, pp. 238-241. |
| Zamzow K.L., et al., “Development and Evaluation of a Wearable Blood Glucose Monitor,” ASAIO Transactions, vol. 36 (3), 1990, pp. M588-M591. |
| Zavalkoff S.R., et al., “Evaluation of Conventional Blood Glucose Monitoring as an Indicator of Integrated Glucose Values Using a Continuous Subcutaneous Sensor,” Diabetes Care, vol. 25(9), 2002, pp. 1603-1606. |
| Zethelius B., et al., “Use of Multiple Biomarkers to Improve the Prediction of Death From Cardiovascular Causes,” N. Engl. J. Med., vol. 358, May 2008, pp. 2107-2116. |
| Zhang, et al., “Elimination of the Acetaminophen Interference in an Implantable Glucose Sensor,” Analytical Chemistry, 1994, vol. 66 (7), pp. 1183-1188. |
| Zhang Y., et al., “Electrochemical Oxidation of H2O2 On Pt and Pt + Ir Electrodes in Physiological Buffer and its Applicability to H2O2-Based Biosensors,” J. Electro Analytical Chemistry, vol. 345, 1993, pp. 253-271. |
| Zhang Y., et al., “In Vitro and In Vivo Evaluation of Oxygen Effects on a Glucose Oxidase Based Implantable Glucose Sensor,” Analytica Chimica Acta, vol. 281, 1993, pp. 513-520. |
| Zhu, et al., “Fabrication and Characterization of Glucose Sensors Based on a Microarray H2O2 Electrode,” Biosensors & Bioelectronics, 1994, vol. 9, pp. 295-300. |
| Zhu, et al., “Planar Amperometric Glucose Sensor Based on Glucose Oxidase Immobilized by Chitosan Film on Prussian blue Layer,” Sensors, 2002, vol. 2, pp. 127-136. |
| Ziaie, et al., “A Single-Channel Implantable Microstimulator for Functional Neuromuscular Stimulation,” IEEE Transactions on Biomedical Engineering, 1997, vol. 44(10), pp. 909-920. |
| Number | Date | Country | |
|---|---|---|---|
| 20190365330 A1 | Dec 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16011414 | Jun 2018 | US |
| Child | 16540000 | US | |
| Parent | 15894573 | Feb 2018 | US |
| Child | 16011414 | US | |
| Parent | 15595808 | May 2017 | US |
| Child | 15894573 | US | |
| Parent | 15197349 | Jun 2016 | US |
| Child | 15595808 | US | |
| Parent | 13181341 | Jul 2011 | US |
| Child | 15197349 | US | |
| Parent | 10648849 | Aug 2003 | US |
| Child | 13181341 | US |
