Nonlinear mapping technique for a physiological characteristic sensor

Information

  • Patent Grant
  • 10001450
  • Patent Number
    10,001,450
  • Date Filed
    Friday, April 18, 2014
    10 years ago
  • Date Issued
    Tuesday, June 19, 2018
    6 years ago
Abstract
A method of measuring blood glucose of a patient is presented here. In accordance with certain embodiments, the method applies a constant voltage potential to a glucose sensor and obtains a constant potential sensor current from the glucose sensor, wherein the constant potential sensor current is generated in response to applying the constant voltage potential to the glucose sensor. The method continues by performing an electrochemical impedance spectroscopy (EIS) procedure for the glucose sensor to obtain EIS output measurements. The method also performs a nonlinear mapping operation on the constant potential sensor current and the EIS output measurements to generate a blood glucose value.
Description
TECHNICAL FIELD

Embodiments of the subject matter described herein relate generally to physiological characteristic sensors, such as glucose sensors. More particularly, embodiments of the subject matter relate to a calibration free blood glucose sensor that utilizes nonlinear mapping techniques.


BACKGROUND

The prior art is replete with sensors, systems, and medical devices that are designed to measure, process, monitor, and/or display physiological characteristics of a patient. For example, the prior art includes glucose sensor devices and systems that monitor blood glucose levels in a subject's body on a continuing basis. Presently, a patient can measure his/her blood glucose (BG) using a BG measurement device, which may be: a glucose meter such as a test strip meter; a continuous glucose measurement system or monitor; a hospital hemacue; or the like. BG measurement devices use various methods to measure the BG level of a patient, such as a sample of the patient's blood, a sensor in contact with a bodily fluid, an optical sensor, an enzymatic sensor, or a fluorescent sensor. When the BG measurement device has generated a BG measurement, the measurement can be output, displayed, processed, or otherwise handled in an appropriate manner.


Currently known continuous glucose measurement systems include subcutaneous (or short-term) sensors and implantable (or long-term) sensors. The current state of the art in continuous glucose monitoring (CGM) is largely adjunctive, meaning that the readings provided by a CGM device (including, e.g., an implantable or subcutaneous sensor) cannot be used without a reference value in order to make a clinical decision. The reference value, in turn, must be obtained from a blood sample, which may be obtained from a BG meter (such as a finger stick device). The reference value can be used to check the accuracy of the sensor, and it can also be used to generate a calibration factor that is applied to the raw sensor data.


The art has searched for ways to eliminate or, at the very least, minimize, the number of finger stick measurements that are necessary for calibration and for assessing sensor health. However, given the number and level of complexity of the multitude of sensor operating modes, no satisfactory solution has been found. At most, diagnostics have been developed that are based on either direct assessment of the sensor output current (Isig), or on comparison of two Isig values. In either case, because the Isig tracks the level of glucose in the body, by definition, it is not analyte independent. As such, by itself, the Isig is not a reliable source of information for sensor diagnostics, nor is it a reliable predictor for continued sensor performance.


Accordingly, it is desirable to have an improved physiological characteristic sensor and related sensor system that addresses the shortcomings of traditional sensor systems. In addition, it is desirable to have a calibration free BG sensor that need not rely on BG finger stick samples. Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.


BRIEF SUMMARY

A method of measuring blood glucose of a patient is provided here. The method applies a constant voltage potential to a glucose sensor and obtains a constant potential sensor current from the glucose sensor. The constant potential sensor current is generated in response to applying the constant voltage potential to the glucose sensor. The method continues by performing an electrochemical impedance spectroscopy (EIS) procedure for the glucose sensor to obtain EIS output measurements, and by performing a nonlinear mapping operation on the constant potential sensor current and the EIS output measurements to generate a blood glucose value.


An exemplary embodiment of a sensor system is also presented here. The sensor system includes a sensor electrode and sensor electronics coupled to the sensor electrode. The sensor electronics apply a constant voltage potential to the sensor electrode, and obtain a constant potential sensor current from the sensor electrode, wherein the constant potential sensor current is generated in response to applying the constant voltage potential to the sensor electrode. The sensor electronics also perform an EIS procedure for the sensor electrode to obtain EIS output measurements, and perform a nonlinear mapping operation on the constant potential sensor current and the EIS output measurements to generate a sensor output value.


Also provided is an exemplary embodiment of a sensor system having a processor-readable storage medium having executable instructions stored thereon. The executable instructions implement a method that obtains a constant potential sensor current from a glucose sensor, wherein the constant potential sensor current is obtained in response to application of a constant voltage potential to the glucose sensor. The method continues by obtaining EIS output measurements from the glucose sensor, wherein the EIS output measurements are obtained in response to application of alternating current (AC) voltage signals to the glucose sensor. The method also performs a nonlinear mapping operation on the constant potential sensor current and the EIS output measurements to generate a blood glucose value.


This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.





BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following figures, wherein like reference numbers refer to similar elements throughout the figures.



FIG. 1 is a schematic representation of a physiological characteristic sensor system configured in accordance with an exemplary embodiment;



FIG. 2 is a schematic representation of a BG sensor system according to an exemplary embodiment;



FIG. 3 is a flow chart that illustrates an exemplary embodiment of a nonlinear modeling process; and



FIG. 4 is a flow chart that illustrates an exemplary embodiment of a BG measurement process.





DETAILED DESCRIPTION

The following detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments. As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description.


Techniques and technologies may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components, processing logic, or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices.


When implemented in software or firmware, various elements of the systems described herein are essentially the code segments or instructions that perform the various tasks. In certain embodiments, the program or code segments are stored in a tangible processor-readable medium, which may include any medium that can store or transfer information. Examples of a non-transitory and processor-readable medium include an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an erasable ROM (EROM), a floppy diskette, a CD-ROM, an optical disk, a hard disk, or the like.



FIG. 1 is a schematic representation of a physiological characteristic sensor system 100 configured in accordance with an exemplary embodiment. The sensor system 100 is suitably configured to measure a physiological characteristic of the subject, e.g., a human patient. In accordance with the non-limiting embodiments presented here, the physiological characteristic of interest is blood glucose, and the sensor system 100 generates output that is indicative of a blood glucose level of the subject. It should be appreciated that the techniques and methodologies described here may also be utilized with other sensor types if so desired.



FIG. 1 depicts a simplified representation of the sensor system 100; in practice the sensor system 100 may include additional elements and functionality that are unrelated or unimportant to the subject matter presented here. Moreover, the sensor system 100 may incorporate or utilize any of the relevant subject matter that is disclosed in the PCT patent application titled APPLICATION OF ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY IN SENSOR SYSTEMS, DEVICES, AND RELATED METHODS, published Dec. 12, 2013 as International Publication Number WO 2013/184416 A2 (the content of which is incorporated by reference herein).


The illustrated embodiment of the sensor system 100 generally includes, without limitation: at least one sensor electrode 102; a signal processor 104; a nonlinear mapper 106; an output interface 108; a regulator 110; and a power supply 112. The elements of the sensor system 100 are coupled together or are otherwise designed to cooperate as needed to support the techniques, methodologies, and operation described in more detail herein. Some or all of the blocks shown in FIG. 1 (e.g., the signal processor 104, the nonlinear mapper 106, and the regulator 110) may include, cooperate with, or be implemented as software, firmware, and/or processing logic. To this end, the sensor system 100 may include one or more processors and one or more processor-readable storage media having executable instructions stored thereon. The executable instructions, when executed by a processor, are capable of implementing the various methods, processes, and techniques described in more detail below. For example, the nonlinear mapper 106 may be realized using suitably written instructions that perform the desired mapping functions.


The elements depicted in FIG. 1 can be implemented and realized in a variety of different ways, depending on the desired application, device platform, and operating environment. For example, all of blocks illustrated in FIG. 1 could be integrated into a single device or component, such as a glucose sensor device that communicates with a monitor device, an insulin pump device, or a computer. As another example, some of the illustrated blocks (such as the signal processor 104, the nonlinear mapper 106, and the output interface 108) could be implemented in a physically distinct device that communicates with a glucose sensor device that houses the sensor electrodes 102, the regulator, and the power supply 112. These and other implementation and deployment options are contemplated by this disclosure.


The sensor electrodes 102 are designed for subcutaneous placement at a selected site in the body of a user. When placed in this manner, the sensor electrodes 102 are exposed to the user's bodily fluids such that they can react in a detectable manner to the physiological characteristic of interest, e.g., blood glucose level. In certain embodiments, the sensor electrodes 102 may include a counter electrode, a reference electrode, and one or more working electrodes. For the embodiments described here, the sensor electrodes 102 employ thin film electrochemical sensor technology of the type used for monitoring blood glucose levels in the body. Further description of flexible thin film sensors of this general type are found in U.S. Pat. No. 5,391,250, entitled METHOD OF FABRICATING THIN FILM SENSORS, which is herein incorporated by reference. In other embodiments, different types of implantable sensor technology, such as chemical based, optical based, or the like, may be used.


The sensor electrodes 102 cooperate with sensor electronics, which may be integrated with the sensor electrodes 102 in a sensor device package, or which may be implemented in a physically distinct device or component that communicates with the sensor electrodes 102 (such as a monitor device, an infusion pump device, a controller device, or the like). In this regard, any or all of the remaining elements shown in FIG. 1 may be included in the sensor electronics, as needed to support the particular embodiment.


For purposes of this example, the sensor electronics include the signal processor 104, the nonlinear mapper 106, the output interface 108, the regulator 110, and the power supply 112. The power supply 112 provides power (in the form of either a voltage, a current, or a voltage including a current) to the regulator 110. The power supply 112 may also be suitably configured to provide operating power to the signal processor 104, the nonlinear mapper 106, and/or the output interface 108 as needed. In certain embodiments, the power supply 112 is realized using one or more batteries.


The regulator 110 generates and applies regulated voltage to the sensor electrodes 102. In certain embodiments, the regulator 110 applies voltage to the counter electrode of the sensor electrodes 102. As described in more detail below, the regulator 110 generates and applies DC voltage to the sensor electrodes 102 during a first excitation mode to obtain a constant potential sensor current (Isig) that is indicative of the blood glucose level. In addition, the regulator 110 generates and applies AC voltage (at different frequencies) to the sensor electrodes 102 during an electrochemical impedance spectroscopy (EIS) excitation mode to carry out an EIS procedure during which EIS output measurements are obtained from the sensor electrodes 102. Thus, the regulator 110 is responsible for managing the excitation voltage characteristics, frequencies, magnitudes, and timing required to support the sensor operating methodologies described herein.


When driven by an excitation voltage signal 120, the sensor electrodes 102 respond in a way that is indicative of a concentration of a physiological characteristic being measured. For this example, the sensor output signal 122 may be indicative of a blood glucose reading. In certain embodiments, the sensor output signal 122 is present at the working electrode of the sensor electrodes 102. In practice, the sensor output signal 122 may be a current or a voltage measured at the working electrode. During an EIS procedure, the sensor output signal 122 is indicative of an impedance at the given frequency, an amplitude, and a phase angle.


The signal processor 104 receives the sensor output signals 122 that are produced in response to the application of corresponding DC or AC voltage to the sensor electrodes 102. The signal processor 104 processes the sensor output signals 122 and generates processed sensor signals that are suitable for use as inputs to the nonlinear mapper 106. The nonlinear mapper 106 receives the processed sensor signals and performs a nonlinear mapping operation to generate a corresponding blood glucose value. The nonlinear mapper 106 utilizes a sensor characterization model for the particular type of sensor, wherein the model generates the blood glucose value in the absence of any calibration factor or linear translation. In this regard, the nonlinear mapper 106 is designed and programmed in a way that accurately generates blood glucose values in a calibration-free manner that does not require BG meter (finger stick) measurements. Moreover, the nonlinear mapper 106 is designed and programmed such that the output mapping automatically compensates for typical manufacturing tolerances, shelf life, operating age, and other changes to the sensor system 100 that would normally be corrected by way of frequent calibration routines.


The BG values generated by the nonlinear mapper 106 may be provided to the output interface 108, which in turn may generate an appropriate output that conveys the BG values. For example, the output interface 108 may include or cooperate with a display driver and graphics processor to render the BG values on a display element (not shown). As another example, the output interface 108 may include or cooperate with a data communication module, such as a network interface, a wireless transmitter, a modem, or the like. The output interface 108 can be designed to support any output format or methodology as appropriate to the particular embodiment. In this regard, the output interface 108 may communicate with any or all of the following, without limitation: a display device; a computer; a pager; a television set; a server; a mobile telephone device; an infusion pump including a display; a personal medical device; hospital equipment; or the like.



FIG. 2 is a schematic representation of a BG sensor system 200 according to an exemplary embodiment. The system 200 depicted in FIG. 2 may correspond to the sensor system 100 depicted in FIG. 1 (but illustrated in a different manner). FIG. 2 focuses more on the nonlinear mapping functionality of the system 200. Accordingly, conventional elements that are unimportant or unrelated to the nonlinear mapping feature are not shown in FIG. 2. For this particular embodiment, the BG sensor system 200 includes, without limitation: a glucose sensor 202; the nonlinear mapper 106; a temperature sensor 206; and a mixer or combiner element 208. These elements cooperate to generate output data that is indicative of a BG value 210 for the monitored subject.


The glucose sensor 202 may include or cooperate with sensor electrodes, a power supply, a regulator, a signal processor, and/or other features or components as described above with reference to FIG. 1. The glucose sensor 202 is preferably realized as an electrochemical component that reacts to glucose levels within the body of the subject. As is well known to those familiar with glucose sensor technology, the glucose sensor 202 may employ a glucose oxidase (GOx) enzyme for catalyzing a reaction with the sensor electrodes. The reaction is responsive to electrical stimulation of the glucose sensor 202, and characteristics of the output signals of the glucose sensor 202 are indicative of the current BG level.


The glucose sensor 202 can be operated in at least two stimulation modes: a DC stimulation mode during which a constant voltage potential is applied to the glucose sensor 202; and an AC stimulation mode during which an alternating current voltage signal is applied to the glucose sensor 202. In some embodiments, the DC stimulation mode and the AC stimulation mode are active at different times. In other embodiments, the DC stimulation mode and the AC stimulation mode occur concurrently. Moreover, the DC stimulation mode may have a different timing scheme relative to the AC stimulation mode. For example, the glucose sensor 202 may be operated in the DC stimulation mode once every five minutes, and operated in the AC stimulation mode once every thirty minutes. Furthermore, the duration of the DC stimulation mode need not be the same as the duration of the AC stimulation mode. In certain embodiments, the AC stimulation mode requires more time than the DC stimulation mode because AC signals having a plurality of different frequencies are applied to the glucose sensor 202 during the AC stimulation mode.


The glucose sensor 202 reacts to a DC voltage in a way that is influenced by the BG level in the body of the subject. The resulting constant potential sensor current (referred to herein as “Isig”) serves as the raw sensor output during the DC stimulation mode. Thus, Isig varies in accordance with changes to the BG level of the subject. As depicted in FIG. 2, the Isig values may serve as one input to the combiner element 208. In accordance with many traditional glucose measurement approaches, Isig values are subjected to a linear calibration factor (which must be updated frequently during the life of the glucose sensor) to obtain an estimated BG value that accurately tracks the subject's actual blood glucose level. In contrast, the raw Isig values 216 obtained from the glucose sensor 202 need not be adjusted by any calibration factor.


As mentioned above, the glucose sensor 202 is also operated in an AC stimulation mode to obtain additional output measurements. The AC stimulation mode corresponds to an electrochemical impedance spectroscopy (EIS) procedure, which is performed for the glucose sensor 202. The glucose sensor 202 responds to the EIS procedure such that EIS output measurements 220 can be obtained. The EIS procedure is performed independently of the DC stimulation mode in that the AC voltage signals associated with the EIS procedure are applied to the glucose sensor 202 at different times than the DC voltage signals. Moreover, the timing associated with the application of the DC voltage signals and the AC voltage signals may vary. For example, the DC stimulation mode may be performed once every five minutes, while the AC stimulation mode may be performed once every thirty minutes.


For this particular embodiment, the EIS procedure is performed for a plurality of different frequencies. Accordingly, the glucose sensor 202 responds to each AC voltage signal such that a respective set of EIS output measurements 220 are obtained. The EIS output measurements 220 for all of the different AC frequencies can be collected and used as additional inputs to the combiner element 208 (as schematically depicted in FIG. 2).


EIS techniques and technology in the context of blood glucose measurement are described in more detail in the PCT patent application published as International Publication Number WO 2013/184416 A2 (the content of which is incorporated by reference herein). In this regard, EIS provides information in the form of sensor impedance and impedance-related parameters at different frequencies. Moreover, for certain ranges of frequencies, impedance and/or impedance-related data are substantially glucose independent. Such glucose independence enables the use of a variety of EIS-based markers or indicators for not only producing a robust, highly-reliable sensor glucose value (through fusion methodologies), but also assessing the condition, health, age, and efficiency of individual electrode(s) and of the overall sensor substantially independently of the glucose-dependent Isig.


EIS, or AC impedance methods, study the system response to the application of a periodic small amplitude AC signal. As is known, impedance may be defined in terms of its magnitude and phase, where the magnitude (|Z|) is the ratio of the voltage difference amplitude to the current amplitude, and the phase (θ) is the phase shift by which the current is ahead of the voltage. When a circuit is driven solely with direct current (DC), the impedance is the same as the resistance, i.e., resistance is a special case of impedance with zero phase angle. However, as a complex quantity, impedance may also be represented by its real and imaginary parts. In this regard, the real and imaginary impedance can be derived from the impedance magnitude and phase.


In performing the EIS procedure and analysis, an AC voltage of various frequencies and a DC bias may be applied between, e.g., the working and reference electrodes of the glucose sensor 202. Although, generally, the EIS procedure may be performed at frequencies in the μHz to MHz range, in certain embodiments, a narrower range of frequencies (e.g., between about 0.1 Hz and about 8 kHz) may be sufficient. Thus, AC potentials may be applied that fall within a frequency range of between about 0.1 Hz and about 8 kHz, with a programmable amplitude of up to at least 100 mV, and preferably at about 50 mV.


EIS may be used in sensor systems where the sensor includes a single working electrode, as well those in which the sensor includes multiple (redundant) working electrodes. In some embodiments, EIS provides valuable information regarding the age (or aging) of the glucose sensor 202. Specifically, at different frequencies, the magnitude and the phase angle of the impedance vary. Moreover, a new sensor normally has higher impedance than a used sensor. Thus, the EIS output measurements can be used to determine information related to the age of the sensor under observation.


The system 200 may also include or cooperate with an optional temperature sensor 206 that provides temperature measurement data 224 as an additional input to the combiner element 208. The temperature measurement data 224 can be used as an additional parameter to generate the blood glucose value 210 with more accuracy. It should be appreciated that the temperature sensor 206 need not be employed, and that the nonlinear mapper 204 can be configured in an appropriate manner to contemplate embodiments that do not use the temperature sensor 206.


The combiner element 208 obtains the Isig values 216, the EIS output measurements 220, and (if available) the temperature measurement data 224 to be used for each calculation of a corresponding BG value 210. The combiner element 208 combines these inputs as needed to accomplish the nonlinear mapping operation (performed by the nonlinear mapper 106). Although FIG. 2 depicts the combiner element 208 as a distinct block, it should be appreciated that the functionality of the combiner element 208 and the nonlinear mapper 106 could be integrated if so desired.


In certain embodiments, each BG value 210 generated by the nonlinear mapper 106 is derived from a combination of at least one Isig value and at least some of the EIS output measurements obtained for an iteration of the EIS procedure. The nonlinear mapper 106, which was described above in the context of FIG. 1, employs a sensor characterization model that accurately produces the BG values 210. The sensor characterization model accurately estimates the subject's actual BG level in a way that compensates for manufacturing variances, age of the glucose sensor 202, and possibly other factors, without requiring any calibrations based on BG meter readings.


The sensor characterization model used by the nonlinear mapper 106 can be determined based on empirical measurement data, corresponding BG meter data, and the like. In this regard, FIG. 3 is a flow chart that illustrates an exemplary embodiment of a nonlinear modeling process 300, which may be performed during the design, development, and/or manufacturing of a glucose system of the type described here. The various tasks performed in connection with the process 300 may be performed by software, hardware, firmware, or any combination thereof. For illustrative purposes, the following description of the process 300 may refer to elements mentioned above in connection with FIGS. 1-2. It should be appreciated that the process 300 may include any number of additional or alternative tasks, the tasks shown in FIG. 3 need not be performed in the illustrated order, and the process 300 may be incorporated into a more comprehensive procedure or process having additional functionality not described in detail herein. Moreover, one or more of the tasks shown in FIG. 3 could be omitted from an embodiment of the process 300 as long as the intended overall functionality remains intact.


The process 300 may involve the collection (task 302) of sensor measurement data and corresponding BG measurements (taken, from a finger stick device or other blood sampling device). The collected data may correspond to any number of different sampling/measurement times. The collected data is then analyzed and processed to calculate a corresponding nonlinear mapping model (task 304). Ideally, the model will accurately output the actual measured BG values using the sensor measurement data as the input. In practice, however, the calculated model will be tested or otherwise verified to ensure that the nonlinear mapping algorithm satisfies the desired accuracy metrics (task 306).


If the calculated model is satisfactory and no modifications are needed (the “No” branch of query task 308), then the process 300 saves or stores the nonlinear mapping model in association with the nonlinear mapper (task 310). If, however, the calculated model needs to be modified or refined in some way, then the process 300 updates certain parameters of the nonlinear mapping model in an attempt to improve its accuracy (task 312). Thereafter, a new nonlinear mapping model is calculated (during the next iteration of task 304) and the process 300 continues as described above.


A different nonlinear mapping model can be generated for each sensor type, model, and/or configuration. Ideally, the nonlinear mapping model will consider and contemplate changes to the electrochemical properties and characteristics of the sensor, which may be influenced by the age of the sensor, by manufacturing variations, by the operating conditions or environment, and the like. Thus, the same nonlinear mapping model can be programmed for use in connection with all sensors to be manufactured and sold under the same model number, SKU, etc.



FIG. 4 is a flow chart that illustrates an exemplary embodiment of a BG measurement process 400, which may be performed by a sensor system of the type described above. The various tasks performed in connection with the process 400 may be performed by software, hardware, firmware, or any combination thereof. For illustrative purposes, the following description of the process 400 may refer to elements mentioned above in connection with FIGS. 1-3. It should be appreciated that the process 400 may include any number of additional or alternative tasks, the tasks shown in FIG. 4 need not be performed in the illustrated order, and the process 400 may be incorporated into a more comprehensive procedure or process having additional functionality not described in detail herein. Moreover, one or more of the tasks shown in FIG. 4 could be omitted from an embodiment of the process 400 as long as the intended overall functionality remains intact.


The measurement process 400 assumes that the nonlinear mapper of the glucose sensor system has been properly trained and configured as described above with reference to the modeling process 300. The process 400 applies a constant DC voltage potential to the glucose sensor (task 402). As explained above, DC voltage is applied to the sensor electrodes during the DC stimulation mode of the glucose sensor. In response to the applied DC voltage, the process 400 obtains the corresponding constant potential sensor current (Isig) from the glucose sensor (task 404).


Thereafter, the glucose sensor is operated in the AC stimulation mode to perform an EIS procedure (task 406). In association with the EIS procedure, AC voltage signals having different frequencies are applied to the glucose sensor. Although the number of different frequencies and the frequency range may vary from one embodiment to another, this non-limiting example utilizes 22 different frequencies. In response to the applied AC voltage signals, the process 400 obtains the corresponding EIS output measurements (task 408) for each of the different frequencies. If available and supported by the particular embodiment, the process 400 also obtains temperature measurement data (task 410) from one or more temperature sensors.


The Isig information, the EIS output measurements, and (if applicable) the temperature measurements can be combined, mixed, conditioned, filtered, or otherwise processed as needed (task 412). Task 412 may be performed to prepare the obtained data for nonlinear mapping. In this regard, the process 400 performs the nonlinear mapping operation (task 414) as defined by the particular sensor characterization model. For this example, nonlinear mapping is performed on the Isig, EIS output measurement, and temperature measurement information associated with the current sampling point or time. Notably, the nonlinear mapping operation is effective at generating a BG value corresponding to the obtained measurement data, in the absence of any linear calibration factors. The calculated BG value produced by the nonlinear mapper can be communicated, output, displayed, saved, or otherwise handled as desired (task 416). The process 400 can be repeated as often as needed to obtain updated BG values in an ongoing manner.


To summarize, nonlinear mapping knowledge is utilized to convert glucose sensor measurement data into accurate BG values. Traditionally, the constant potential electrical signals (Isig) have been mapped in a linear fashion to obtain estimated BG output. In order to overcome divergence in sensor manufacturing, sensor insertion site, patient specific influences, temperature, and many other influences, such conventional glucose sensors need to be calibrated at least once every twelve hours, and a calibration at the beginning of the sensor life is mandatory.


In contrast to traditional approaches, a glucose system of the type described in detail above employs advanced EIS technology that generates frequency dependent impedance electrical signals from the sensor electrode, which in turn can be related to physical quantities or processes such as the membrane resistance or the diffusion characteristics of the materials in the sensor. In addition, the sensor system can potentially collect temperature measurements. This information in addition to the Isig measurements can be nonlinearly mapped into BG values without the need of calibration, and despite the various sensor divergences mentioned previously. The disclosed technology can be implemented to produce a calibration-free continuous glucose sensor that automatically responds to changes in the electrochemical characteristics of the sensor over time.


While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application.

Claims
  • 1. A method of measuring blood glucose of a patient, the method comprising: applying a constant voltage potential to a glucose sensor;obtaining a constant potential sensor current from the glucose sensor, wherein the constant potential sensor current is generated in response to applying the constant voltage potential to the glucose sensor;performing an electrochemical impedance spectroscopy (EIS) procedure for the glucose sensor to obtain EIS output measurements; andperforming a nonlinear mapping operation on the constant potential sensor current and the EIS output measurements to generate a blood glucose value.
  • 2. The method of claim 1, wherein the nonlinear mapping operation generates the blood glucose value in the absence of any calibration factor for the glucose sensor.
  • 3. The method of claim 1, wherein applying the constant voltage potential and performing the EIS procedure occur at different times.
  • 4. The method of claim 1, wherein performing the EIS procedure comprises: applying an alternating current (AC) voltage signal to the glucose sensor.
  • 5. The method of claim 1, wherein the EIS procedure is performed for a plurality of frequencies.
  • 6. The method of claim 1, further comprising: obtaining temperature measurement data, wherein the nonlinear mapping operation is performed on the constant potential sensor current, the EIS output measurements, and the temperature measurement data to generate the blood glucose value.
  • 7. The method of claim 1, wherein the nonlinear mapping operation is associated with a sensor characterization model for the glucose sensor.
  • 8. The method of claim 1, wherein the nonlinear mapping operation comprises: combining the constant potential sensor current and at least some of the EIS output measurements.
  • 9. A sensor system comprising: A sensor electrode; andSensor electronics coupled to the sensor electrode, the sensor electronics including one or more processors having executable instructions stored thereon, the executable instructions executed by the processor to:Apply a constant voltage potential to the sensor electrode;Obtain a constant potential sensor current from the sensor electrode, wherein the constant potential sensor current is generated in response to applying the constant voltage potential to the sensor electrode;Perform an electrochemical impedance spectroscopy (EIS) procedure for the sensor electrode to obtain EIS output measurements; andPerform a nonlinear mapping operation on the constant potential sensor current and the EIS output measurements to generate a sensor output value.
  • 10. The sensor system of claim 9, wherein: the sensor electrode comprises a glucose sensor electrode; andthe sensor output value comprises a blood glucose value.
  • 11. The sensor system of claim 9, wherein applying the constant voltage potential and performing the EIS procedure occur concurrently.
  • 12. The sensor system of claim 9, wherein the sensor electronics obtains temperature measurement data, wherein the nonlinear mapping operation is performed on the constant potential sensor current, the EIS output measurements, and the temperature measurement data to generate the blood glucose value.
  • 13. The sensor system of claim 9, wherein the nonlinear mapping operation is associated with a sensor characterization model for the glucose sensor.
  • 14. The sensor system of claim 9, wherein the nonlinear mapping operation comprises: combining the constant potential sensor current and at least some of the EIS output measurements.
  • 15. A sensor system comprising a sensor electrode, one or more processors, and a processor-readable storage medium having executable instructions stored thereon, wherein the executable instructions implement a method comprising: Obtaining a constant potential sensor current from a glucose sensor, wherein the constant potential current is obtained in response to application of a constant voltage potential to the glucose sensor;Obtaining electrochemical impedance spectroscopy (EIS) output measurement from the glucose sensor, wherein the EIS output measurements are obtained in response to application of alternating current (AC) voltage signals to the glucose sensor; andPerforming a nonlinear mapping operation on the constant potential sensor current and the EIS output measurement to generate a blood glucose value.
  • 16. The sensor system of claim 15, wherein the nonlinear mapping operation generates the blood glucose value in the absence of any calibration factor for the glucose sensor.
  • 17. The sensor system of claim 15, wherein the method implemented by the executable instructions further comprises: obtaining temperature measurement data, wherein the nonlinear mapping operation is performed on the constant potential sensor current, the EIS output measurements, and the temperature measurement data to generate the blood glucose value.
  • 18. The sensor system of claim 15, wherein the executable instructions define a sensor characterization model for the glucose sensor.
  • 19. The sensor system of claim 15, wherein the nonlinear mapping operation comprises: combining the constant potential sensor current and at least some of the EIS output measurements.
  • 20. The sensor system of claim 15, wherein the glucose sensor is integrated with the sensor system.
US Referenced Citations (237)
Number Name Date Kind
3631847 Hobbs, II Jan 1972 A
4212738 Henne Jul 1980 A
4270532 Franetzki et al. Jun 1981 A
4282872 Franetzki et al. Aug 1981 A
4373527 Fischell Feb 1983 A
4395259 Prestele et al. Jul 1983 A
4433072 Pusineri et al. Feb 1984 A
4443218 Decant, Jr. et al. Apr 1984 A
4494950 Fischell Jan 1985 A
4542532 McQuilkin Sep 1985 A
4550731 Batina et al. Nov 1985 A
4559037 Franetzki et al. Dec 1985 A
4562751 Nason et al. Jan 1986 A
4671288 Gough Jun 1987 A
4678408 Nason et al. Jul 1987 A
4685903 Cable et al. Aug 1987 A
4731051 Fischell Mar 1988 A
4731726 Allen, III Mar 1988 A
4781798 Gough Nov 1988 A
4803625 Fu et al. Feb 1989 A
4809697 Causey, III et al. Mar 1989 A
4826810 Aoki May 1989 A
4871351 Feingold Oct 1989 A
4898578 Rubalcaba, Jr. Feb 1990 A
5003298 Havel Mar 1991 A
5011468 Lundquist et al. Apr 1991 A
5019974 Beckers May 1991 A
5050612 Matsumura Sep 1991 A
5078683 Sancoff et al. Jan 1992 A
5080653 Voss et al. Jan 1992 A
5097122 Colman et al. Mar 1992 A
5100380 Epstein et al. Mar 1992 A
5101814 Palti Apr 1992 A
5108819 Heller et al. Apr 1992 A
5153827 Coutre et al. Oct 1992 A
5165407 Wilson et al. Nov 1992 A
5247434 Peterson et al. Sep 1993 A
5262035 Gregg et al. Nov 1993 A
5262305 Heller et al. Nov 1993 A
5264104 Gregg et al. Nov 1993 A
5264105 Gregg et al. Nov 1993 A
5284140 Allen et al. Feb 1994 A
5299571 Mastrototaro Apr 1994 A
5307263 Brown Apr 1994 A
5317506 Coutre et al. May 1994 A
5320725 Gregg et al. Jun 1994 A
5322063 Allen et al. Jun 1994 A
5338157 Blomquist Aug 1994 A
5339821 Fujimoto Aug 1994 A
5341291 Roizen et al. Aug 1994 A
5350411 Ryan et al. Sep 1994 A
5356786 Heller et al. Oct 1994 A
5357427 Langen et al. Oct 1994 A
5368562 Blomquist et al. Nov 1994 A
5370622 Livingston et al. Dec 1994 A
5371687 Holmes, II et al. Dec 1994 A
5376070 Purvis et al. Dec 1994 A
5390671 Lord et al. Feb 1995 A
5391250 Cheney, II et al. Feb 1995 A
5403700 Heller et al. Apr 1995 A
5411647 Johnson et al. May 1995 A
5482473 Lord et al. Jan 1996 A
5485408 Blomquist Jan 1996 A
5505709 Funderburk et al. Apr 1996 A
5497772 Schulman et al. May 1996 A
5543326 Heller et al. Aug 1996 A
5569186 Lord et al. Oct 1996 A
5569187 Kaiser Oct 1996 A
5573506 Vasko Nov 1996 A
5582593 Hultman Dec 1996 A
5586553 Halili et al. Dec 1996 A
5593390 Castellano et al. Jan 1997 A
5593852 Heller et al. Jan 1997 A
5594638 Illiff Jan 1997 A
5609060 Dent Mar 1997 A
5626144 Tacklind et al. May 1997 A
5630710 Tune et al. May 1997 A
5643212 Coutre et al. Jul 1997 A
5660163 Schulman et al. Aug 1997 A
5660176 Iliff Aug 1997 A
5665065 Colman et al. Sep 1997 A
5665222 Heller et al. Sep 1997 A
5685844 Marttila Nov 1997 A
5687734 Dempsey et al. Nov 1997 A
5704366 Tacklind et al. Jan 1998 A
5750926 Schulman et al. May 1998 A
5754111 Garcia May 1998 A
5764159 Neftel Jun 1998 A
5772635 Dastur et al. Jun 1998 A
5779665 Mastrototaro et al. Jul 1998 A
5788669 Peterson Aug 1998 A
5791344 Schulman et al. Aug 1998 A
5800420 Gross et al. Sep 1998 A
5807336 Russo et al. Sep 1998 A
5814015 Gargano et al. Sep 1998 A
5822715 Worthington et al. Oct 1998 A
5832448 Brown Nov 1998 A
5840020 Heinonen et al. Nov 1998 A
5861018 Feierbach et al. Jan 1999 A
5868669 Iliff Feb 1999 A
5871465 Vasko Feb 1999 A
5879163 Brown et al. Mar 1999 A
5885245 Lynch et al. Mar 1999 A
5897493 Brown Apr 1999 A
5899855 Brown May 1999 A
5904708 Goedeke May 1999 A
5913310 Brown Jun 1999 A
5917346 Gord Jun 1999 A
5918603 Brown Jul 1999 A
5925021 Castellano et al. Jul 1999 A
5933136 Brown Aug 1999 A
5935099 Peterson et al. Aug 1999 A
5940801 Brown Aug 1999 A
5956501 Brown Sep 1999 A
5960403 Brown Sep 1999 A
5965380 Heller et al. Oct 1999 A
5972199 Heller et al. Oct 1999 A
5978236 Faberman et al. Nov 1999 A
5997476 Brown Dec 1999 A
5999848 Gord et al. Dec 1999 A
5999849 Gord Dec 1999 A
6009339 Bentsen et al. Dec 1999 A
6032119 Brown et al. Feb 2000 A
6043437 Schulman et al. Mar 2000 A
6081736 Colvin et al. Jun 2000 A
6083710 Heller et al. Jul 2000 A
6088608 Schulman et al. Jul 2000 A
6101478 Brown Aug 2000 A
6103033 Say et al. Aug 2000 A
6119028 Schulman et al. Sep 2000 A
6120676 Heller et al. Sep 2000 A
6121009 Heller et al. Sep 2000 A
6134461 Say et al. Oct 2000 A
6143164 Heller et al. Nov 2000 A
6162611 Heller et al. Dec 2000 A
6175752 Say et al. Jan 2001 B1
6183412 Benkowski et al. Feb 2001 B1
6246992 Brown Jun 2001 B1
6259937 Schulman et al. Jul 2001 B1
6329161 Heller et al. Dec 2001 B1
6408330 DeLaHuerga Jun 2002 B1
6424847 Mastrototaro et al. Jul 2002 B1
6472122 Schulman et al. Oct 2002 B1
6484045 Holker et al. Nov 2002 B1
6484046 Say et al. Nov 2002 B1
6503381 Gotoh et al. Jan 2003 B1
6514718 Heller et al. Feb 2003 B2
6544173 West et al. Apr 2003 B2
6553263 Meadows et al. Apr 2003 B1
6554798 Mann et al. Apr 2003 B1
6558320 Causey, III et al. May 2003 B1
6558351 Steil et al. May 2003 B1
6560741 Gerety et al. May 2003 B1
6565509 Say et al. May 2003 B1
6579690 Bonnecaze et al. Jun 2003 B1
6591125 Buse et al. Jul 2003 B1
6592745 Feldman et al. Jul 2003 B1
6605200 Mao et al. Aug 2003 B1
6605201 Mao et al. Aug 2003 B1
6607658 Heller et al. Aug 2003 B1
6616819 Liamos et al. Sep 2003 B1
6618934 Feldman et al. Sep 2003 B1
6623501 Heller et al. Sep 2003 B2
6641533 Causey, III et al. Nov 2003 B2
6654625 Say et al. Nov 2003 B1
6659980 Moberg et al. Dec 2003 B2
6671554 Gibson et al. Dec 2003 B2
6676816 Mao et al. Jan 2004 B2
6689265 Heller et al. Feb 2004 B2
6728576 Thompson et al. Apr 2004 B2
6733471 Ericson et al. May 2004 B1
6746582 Heller et al. Jun 2004 B2
6747556 Medema et al. Jun 2004 B2
6749740 Liamos et al. Jun 2004 B2
6752787 Causey, III et al. Jun 2004 B1
6809653 Mann et al. Oct 2004 B1
6881551 Heller et al. Apr 2005 B2
6892085 McIvor et al. May 2005 B2
6893545 Gotoh et al. May 2005 B2
6895263 Shin et al. May 2005 B2
6916159 Rush et al. Jul 2005 B2
6932584 Gray et al. Aug 2005 B2
6932894 Mao et al. Aug 2005 B2
6942518 Liamos et al. Sep 2005 B2
7153263 Carter et al. Dec 2006 B2
7153289 Vasko Dec 2006 B2
7396330 Banet et al. Jul 2008 B2
9510782 Kamath Dec 2016 B2
20010044731 Coffman et al. Nov 2001 A1
20020013518 West et al. Jan 2002 A1
20020055857 Mault et al. May 2002 A1
20020082665 Haller et al. Jun 2002 A1
20020137997 Mastrototaro et al. Sep 2002 A1
20020161288 Shin et al. Oct 2002 A1
20030060765 Campbell et al. Mar 2003 A1
20030078560 Miller et al. Apr 2003 A1
20030088166 Say et al. May 2003 A1
20030144581 Conn et al. Jul 2003 A1
20030152823 Heller Aug 2003 A1
20030176183 Drucker et al. Sep 2003 A1
20030188427 Say et al. Oct 2003 A1
20030199744 Buse et al. Oct 2003 A1
20030208113 Mault et al. Nov 2003 A1
20030220552 Reghabi et al. Nov 2003 A1
20040061232 Shah et al. Apr 2004 A1
20040061234 Shah et al. Apr 2004 A1
20040064133 Miller et al. Apr 2004 A1
20040064156 Shah et al. Apr 2004 A1
20040073095 Causey, III et al. Apr 2004 A1
20040074785 Holker et al. Apr 2004 A1
20040093167 Braig et al. May 2004 A1
20040097796 Berman et al. May 2004 A1
20040102683 Khanuja et al. May 2004 A1
20040111017 Say et al. Jun 2004 A1
20040122353 Shahmirian et al. Jun 2004 A1
20040167465 Mihai et al. Aug 2004 A1
20040263354 Mann et al. Dec 2004 A1
20050038331 Silaski et al. Feb 2005 A1
20050038680 McMahon et al. Feb 2005 A1
20050154271 Rasdal et al. Jul 2005 A1
20050192557 Brauker et al. Sep 2005 A1
20060229694 Schulman et al. Oct 2006 A1
20060238333 Welch et al. Oct 2006 A1
20060240540 Nakatsuka Oct 2006 A1
20060293571 Bao et al. Dec 2006 A1
20070088521 Shmueli et al. Apr 2007 A1
20070135866 Baker et al. Jun 2007 A1
20080033254 Kamath Feb 2008 A1
20080154503 Wittenber et al. Jun 2008 A1
20080262387 List Oct 2008 A1
20090081951 Erdmann et al. Mar 2009 A1
20090082635 Baldus et al. Mar 2009 A1
20120108933 Liang May 2012 A1
20120262298 Bohm Oct 2012 A1
20130060105 Shah Mar 2013 A1
20130183243 LaBelle Jul 2013 A1
20130328573 Yang Dec 2013 A1
Foreign Referenced Citations (29)
Number Date Country
4329229 Mar 1995 DE
0319268 Nov 1988 EP
0806738 Nov 1997 EP
0880936 Dec 1998 EP
1338295 Aug 2003 EP
1631036 Mar 2006 EP
2218831 Nov 1989 GB
WO 9620745 Jul 1996 WO
WO 9636389 Nov 1996 WO
WO 9637246 Nov 1996 WO
WO 9721456 Jun 1997 WO
WO 9820439 May 1998 WO
WO 9824358 Jun 1998 WO
WO 9842407 Oct 1998 WO
WO 9849659 Nov 1998 WO
WO 9859487 Dec 1998 WO
WO 9908183 Feb 1999 WO
WO 9910801 Mar 1999 WO
WO 9918532 Apr 1999 WO
WO 9922236 May 1999 WO
WO 0010628 Mar 2000 WO
WO 0019887 Apr 2000 WO
WO 0048112 Aug 2000 WO
WO 02058537 Aug 2002 WO
PCTUS0203299 Oct 2002 WO
WO 03001329 Jan 2003 WO
WO 03094090 Nov 2003 WO
WO 2005065538 Jul 2005 WO
WO2013184416 Dec 2013 WO
Non-Patent Literature Citations (91)
Entry
(Animas Corporation, 1999). Animas . . . bringing new life to insulin therapy.
Bode B W, et al. (1996). Reduction in Severe Hypoglycemia with Long-Term Continuous Subcutaneous Insulin Infusion in Type I Diabetes. Diabetes Care, vol. 19, No. 4, 324-327.
Boland E (1998). Teens Pumping it Up! Insulin Pump Therapy Guide for Adolescents. 2nd Edition.
Brackenridge B P (1992). Carbohydrate Gram Counting a Key to Accurate Mealtime Boluses in Intensive Diabetes Therapy. Practical Diabetology, vol. 11, No. 2, pp. 22-28.
Brackenridge, B P et al. (1995). Counting Carbohydrates How to Zero in on Good Control. MiniMed Technologies Inc.
Farkas-Hirsch R et al. (1994). Continuous Subcutaneous Insulin Infusion: A Review of the Past and Its Implementation for the Future. Diabetes Spectrum From Research to Practice, vol. 7, No. 2, pp. 80-84, 136-138.
Hirsch I B et al. (1990). Intensive Insulin Therapy for Treatment of Type I Diabetes. Diabetes Care, vol. 13, No. 12, pp. 1265-1283.
Kulkarni K et al. (1999). Carbohydrate Counting a Primer for Insulin Pump Users to Zero in on Good Control. MiniMed Inc.
Marcus A O et al. (1996). Insulin Pump Therapy Acceptable Alternative to Injection Therapy. Postgraduate Medicine, vol. 99, No. 3, pp. 125-142.
Reed J et al. (1996). Voice of the Diabetic, vol. 11, No. 3, pp. 1-38.
Skyler J S (1989). Continuous Subcutaneous Insulin Infusion [CSII] With External Devices: Current Status. Update in Drug Delivery Systems, Chapter 13, pp. 163-183. Futura Publishing Company.
Skyler J S et al. (1995). The Insulin Pump Therapy Book Insights from the Experts. MiniMed⋅Technologies.
Strowig S M (1993). Initiation and Management of Insulin Pump Therapy. The Diabetes Educator, vol. 19, No. 1, pp. 50-60.
Walsh J, et al. (1989). Pumping Insulin: The Art of Using an Insulin Pump. Published by MiniMed⋅Technologies.
(Intensive Diabetes Management, 1995). Insulin Infusion Pump Therapy. Pages 66-78.
Disetronic My Choice™ D-TRON™ Insulin Pump Reference Manual. (no date).
Disetronic H-TRON® plus Quick Start Manual. (no date).
Disetronic My Choice H-TRONplus Insulin Pump Reference Manual. (no date).
Disetronic H-TRON®plus Reference Manual. (no date).
(MiniMed, 1996). The MiniMed 506. 7 pages. Retrieved on Sep. 16, 2003 from the World Wide Web: http://web.archive.org/web/19961111054527/www.minimed.com/files/506_pic.htm.
(MiniMed, 1997). MiniMed 507 Specifications. 2 pages. Retrieved on Sep. 16, 2003 from the World Wide Web: http://web.archive.org/web/19970124234841/www.minimed.com/files/mmn075.htm.
(MiniMed, 1996). FAQ: The Practical Things . . . pp. 1-4. Retrieved on Sep. 16, 2003 from the World Wide Web: http://web.archive.org/web/19961111054546/www.minimed.com/files/faq_pract.htm.
(MiniMed, 1997). Wanted: a Few Good Belt Clips! 1 page. Retrieved on Sep. 16, 2003 from the World Wide Web: http://web.archive.org/web/19970124234559/www.minimed.com/files/mmn002.htm.
(MiniMed Technologies, 1994). MiniMed 506 Insulin Pump User's Guide.
(MiniMed Technologies, 1994). MiniMed™ Dosage Calculator Initial Meal Bolus Guidelines / MiniMed™ Dosage Calculator Initial Basal Rate Guidelines Percentage Method. 4 pages.
(MiniMed, 1996). MiniMed™ 507 Insulin Pump User's Guide.
(MiniMed, 1997). MiniMed™ 507 Insulin Pump User's Guide.
(MiniMed, 1998). MiniMed 507C Insulin Pump User's Guide.
(MiniMed International, 1998). MiniMed 507C Insulin Pump For those who appreciate the difference.
(MiniMed Inc., 1999). MiniMed 508 Flipchart Guide to Insulin Pump Therapy.
(MiniMed Inc., 1999). Insulin Pump Comparison / Pump Therapy Will Change Your Life.
(MiniMed, 2000). MiniMed® 508 User's Guide.
(MiniMed Inc., 2000). MiniMed® Now [I] Can Meal Bolus Calculator / MiniMed® Now [I] Can Correction Bolus Calculator.
(MiniMed Inc., 2000). Now [I] Can MiniMed Pump Therapy.
(MiniMed Inc., 2000). Now [I] Can MiniMed Diabetes Management.
(Medtronic MiniMed, 2002). The 508 Insulin Pump a Tradition of Excellence.
(Medtronic MiniMed, 2002). Medtronic MiniMed Meal Bolus Calculator and Correction Bolus Calculator. International Version.
Abel, P., et al., “Experience with an implantable glucose sensor as a prerequiste of an artificial beta cell,” Biomed. Biochim. Acta 43 (1984) 5, pp. 577-584.
Bindra, Dilbir S., et al., “Design and in Vitro Studies of a Needle-Type Glucose Sensor for a Subcutaneous Monitoring,” American Chemistry Society, 1991, 63, pp. 1692-1696.
Boguslavsky, Leonid, et al., “Applications of redox polymers in biosensors,” Sold State Ionics 60, 1993, pp. 189-197.
Geise, Robert J., et al., “Electropolymerized 1,3-diaminobenzene for the construction of a 1,1′-dimethylferrocene mediated glucose biosensor,” Analytica Chimica Acta, 281, 1993, pp. 467-473.
Gernet, S., et al., “A Planar Glucose Enzyme Electrode,” Sensors and Actuators, 17, 1989, pp. 537-540.
Gernet, S., et al., “Fabrication and Characterization of a Planar Electromechanical Cell and its Application as a Glucose Sensor,” Sensors and Actuators, 18, 1989, pp. 59-70.
Gorton, L., et al., “Amperometric Biosensors Based on an Apparent Direct Electron Transfer Between Electrodes and Immobilized Peroxiases,” Analyst, Aug. 1991, vol. 117, pp. 1235-1241.
Gorton, L., et al., “Amperometric Glucose Sensors Based on Immobilized Glucose-Oxidizing Enymes and Chemically Modified Electrodes,” Analytics Chimica Acta, 249, 1991, pp. 43-54.
Gough, D. A., et al., “Two-Dimensional Enzyme Electrode Sensor for Glucose,” Analytical Chemistry, vol. 57, No. 5, 1985, pp. 2351-2357.
Gregg, Brian A., et al., “Cross-Linked Redox Gels Containing Glucose Oxidase for Amperometric Biosensor Applications,” Analytical Chemistry, 62, pp. 258-263.
Gregg, Brian A., et al., “Redox Polymer Films Containing Enzymes. 1. A Redox-Conducting Epoxy Cement: Synthesis, Characterization, and Electrocatalytic Oxidation of Hydroquinone,” The Journal of Physical Chemistry, vol. 95, No. 15, 1991, pp. 5970-5975.
Hashiguchi, Yasuhiro, MD, et al., “Development of a Miniaturized Glucose Monitoring System by Combining a Needle-Type Glucose Sensor With Microdialysis Sampling Method,” Diabetes Care, vol. 17, No. 5, May 1994, pp. 387-389.
Heller, Adam, “Electrical Wiring of Redox Enzymes,” Acc. Chem. Res., vol. 23, No. 5, May 1990, pp. 128-134.
Jobst, Gerhard, et al., “Thin-Film Microbiosensors for Glucose-Lactate Monitoring,” Analytical Chemistry, vol. 68, No. 18, Sep. 15, 1996, pp. 3173-3179.
Johnson, K.W., et al., “In vivo evaluation of an electroenzymatic glucose sensor implanted in subcutaneous tissue,” Biosensors & Bioelectronics, 7, 1992, pp. 709-714.
Jönsson, G., et al., “An Electromechanical Sensor for Hydrogen Peroxide Based on Peroxidase Adsorbed on a Spectrographic Graphite Electrode,” Electroanalysis, 1989, pp. 465-468.
Kanapieniene, J. J., et al., “Miniature Glucose Biosensor with Extended Linearity,” Sensors and Actuators, B. 10, 1992, pp. 37-40.
Kawamori, Ryuzo, et al., “Perfect Normalization of Excessive Glucagon Responses to Intraveneous Arginine in Human Diabetes Mellitus With the Artificial Beta-Cell,” Diabetes vol. 29, Sep. 1980, pp. 762-765.
Kimura, J., et al., “An Immobilized Enzyme Membrane Fabrication Method,” Biosensors 4, 1988, pp. 41-52.
Koudelka, M., et al., “In-vivo Behaviour of Hypodermically Implanted Microfabricated Glucose Sensors,” Biosensors & Bioelectronics 6, 1991, pp. 31-36.
Koudelka, M., et al., “Planar Amperometric Enzyme-Based Glucose Microelectrode,” Sensors & Actuators, 18, 1989, pp. 157-165.
Mastrototaro, John J., et al., “An electroenzymatic glucose sensor fabricated on a flexible substrate,” Sensors & Actuators, B. 5, 1991, pp. 139-144.
Mastrototaro, John J., et al., “An Electroenzymatic Sensor Capable of 72 Hour Continuous Monitoring of Subcutaneous Glucose,” 14th Annual International Diabetes Federation Congress, Washington D.C., Jun. 23-28, 1991.
McKean, Brian D., et al., “A Telemetry-Instrumentation System for Chronically Implanted Glucose and Oxygen Sensors,” IEEE Transactions on Biomedical Engineering, Vo. 35, No. 7, Jul. 1988, pp. 526-532.
Monroe, D., “Novel Implantable Glucose Sensors,” ACL, Dec. 1989, pp. 8-16.
Morff, Robert J., et al., “Microfabrication of Reproducible, Economical, Electroenzymatic Glucose Sensors,” Annuaal International Conference of teh IEEE Engineering in Medicine and Biology Society, Vo. 12, No. 2, 1990, pp. 483-484.
Moussy, Francis, et al., “Performance of Subcutaneously Implanted Needle-Type Glucose Sensors Employing a Novel Trilayer Coating,” Analytical Chemistry, vol. 65, No. 15, Aug. 1, 1993, pp. 2072-2077.
Nakamoto, S., et al., “A Lift-Off Method for Patterning Enzyme-Immobilized Membranes in Multi-Biosensors,” Sensors and Actuators 13, 1988, pp. 165-172.
Nishida, Kenro, et al., “Clinical applications of teh wearable artifical endocrine pancreas with the newly designed needle-type glucose sensor,” Elsevier Sciences B.V., 1994, pp. 353-358.
Nishida, Kenro, et al., “Development of a ferrocene-mediated needle-type glucose sensor covereed with newly designd biocompatible membrane, 2-methacryloyloxyethylphosphorylcholine -co-n-butyl nethacrylate,” Medical Progress Through Technology, vol. 21, 1995, pp. 91-103.
Poitout, V., et al., “A glucose monitoring system for on line estimation oin man of blood glucose concentration using a miniaturized glucose sensor implanted in the subcutaneous tissue adn a wearable control unit,” Diabetologia, vol. 36, 1991, pp. 658-663.
Reach, G., “A Method for Evaluating in vivo the Functional Characteristics of Glucose Sensors,” Biosensors 2, 1986, pp. 211-220.
Shaw, G. W., et al., “In vitro testing of a simply constructed, highly stable glucose sensor suitable for implantation in diabetic patients,” Biosensors & Bioelectronics 6, 1991, pp. 401-406.
Shichiri, M., “A Needle-Type Glucose Sensor—A Valuable Tool Not Only for a Self-Blood Glucose Monitoring but for a Wearable Artifiical Pancreas,” Life Support Systems Proceedings, XI Annual Meeting ESAO, Alpbach-Innsbruck, Austria, Sep. 1984, pp. 7-9.
Shichiri, Motoaki, et al., “An artificial endocrine pancreas—problems awaiting solution for long-term clinical applications of a glucose sensor,” Frontiers Med. Biol. Engng., 1991, vol. 3, No. 4, pp. 283-292.
Shichiri, Motoaki, et al., “Closed-Loop Glycemic Control with a Wearable Artificial Endocrine Pancreas—Variations in Daily Insulin Requirements to Glycemic Response,” Diabetes, vol. 33, Dec. 1984, pp. 1200-1202.
Shichiri, Motoaki, et al., “Glycaemic Control in a Pacreatectomized Dogs with a Wearable Artificial Endocrine Pancreas,” Diabetologia, vol. 24, 1983, pp. 179-184.
Shichiri, M., et al., “In Vivo Characteristics of Needle-Type Glucose Sensor—Measurements of Subcutaneous Glucose Concentrations in Human Volunteers,” Hormone and Metabolic Research, Supplement Series vol. No. 20, 1988, pp. 17-20.
Shichiri, M., et al., “Membrane design for extending the long-life of an implantable glucose sensor,” Diab. Nutr. Metab., vol. 2, No. 4, 1989, pp. 309-313.
Shichiri, Motoaki, et al., “Normalization of the Paradoxic Secretion of Glucagon in Diabetes VVho Were Controlled by the Artificial Beta Cell,” Diabetes, vol. 28, Apr. 1979, pp. 272-275.
Shichiri, Motoaki, 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, No. 3, May-Jun. 1986, pp. 298-301.
Shichiri, Motoaki, et al., “Wearable Artificial Endocrine Pancreas with Needle-Type Glucose Sensor,” The Lancet, Nov. 20, 1982, pp. 1129-1131.
Shichiri, Motoaki, et al., “The Wearable Artificial Endocrine Pancreas with a Needle-Type Glucose Sensor: Perfect Glycemic Control in Ambulatory Diabetes,” Acta Paediatr Jpn 1984, vol. 26, pgs. 359-370.
Shinkai, Seiji, “Molecular Recognitiion of Mono- and Di-saccharides by Phenylboronic Acids in Solvent Extraction and as a Monolayer,” J. Chem. Soc., Chem. Commun., 1991, pp. 1039-1041.
Shults, Mark C., “A Telemetry-Instrumentation System for Monitoring Multiple Subcutaneously Implanted Glucose Sensors,” IEEE Transactions on Biomedical Engineering, vol. 41, No. 10, Oct. 1994, pp. 937-942.
Sternberg, Robert, et al., “Study and Development of Multilayer Needle-type Enzyme-based Glucose Microsensors,” Biosensors, vol. 4, 1988, pp. 27-40.
Tamiya, E., et al., “Micro Glucose Sensors using Electron Mediators Immobilized on a Polypyrrole-Modified Electrode,” Sensors and Actuators, vol. 18, 1989, pp. 297-307.
Tsukagoshi, Kazuhiko, et al., “Specific Complexation with Mono- and Disaccharides that can be Detected by Circular Dichroism,” J. Org. Chem., vol. 56, 1991, pp. 4089-4091.
Urban, G., et al., “Miniaturized multi-enzyme biosensors integrated with pH sensors on flexible polymer carriers for in vivo applciations,” Biosensors & Bioelectronics, vol. 7, 1992, pp. 733-739.
Ubran, G., et al., “Miniaturized thin-film biosensors using covalently immobilized glucose oxidase,” Biosensors & Bioelectronics, vol. 6, 1991, pp. 555-562.
Velho, G., et al., “In vivo calibration of a subcutaneous glucose sensor for determination of subcutaneous glucose kinetics,” Diab. Nutr. Metab., vol. 3, 1988, pp. 227-233.
Wang, Joseph, et al., “Needle-Type Dual Microsensor for the Simultaneous Monitoring of Glucose and Insulin,” Analytical Chemistry, vol. 73, 2001, pp. 844-847.
Yamasaki, Yoshimitsu, et al., “Direct Measurement of Whole Blood Glucose by a Needle-Type Sensor,” Clinics Chimica Acta, vol. 93, 1989, pp. 93-98.
Yokoyama, K., “Integrated Biosensor for Glucose and Galactose,” Analytica Chimica Acta, vol. 218, 1989, pp. 137-142.
Related Publications (1)
Number Date Country
20150300969 A1 Oct 2015 US