Patent Grant
9585577
Field of the Invention
The present invention relates to medical devices for monitoring cardiovascular properties, e.g. cardiac output (CO), stroke volume (SV), and continuous non-invasive blood pressure (cNIBP).
Description of the Related Art
CO is typically measured in a hospital setting and, informally, indicates how efficiently a patient's heart pumps blood through their arterial tree. More specifically, CO, with units of liters/minute, describes the time-dependent volume of blood ejected from the left ventricle into the aorta; it indicates how well the patient's heart delivers blood-borne oxygen, nutrients, and other substances to the cells in the body. CO is the product of heart rate (HR) and SV, where SV is defined as the mathematical difference between left ventricular end diastolic volume (EDV) and end systolic volume (ESV), i.e.:
CO=SV×HR (1)
Combining CO and mean arterial blood pressure (MAP) into a single value, called ‘cardiac power’ (CP), provides a particularly valuable prognostic variable for monitoring patients suffering from cardiac conditions such as congestive heart failure (CHF), and is an independent predictor of mortality that can be measured non-invasively using cardiopulmonary exercise testing. Specifically, CP is defined as:
CP=CO×MAP (2)
Measuring CO and SV in a continuous, non-invasive manner with high clinical accuracy has often been considered a ‘holy grail’ of medical-device monitoring. Most existing techniques in this field require in-dwelling catheters, which in turn can harm the patient, are inherently inaccurate in the critically, and require a specially trained operator. For example, current ‘gold standards’ for this measurement are thermodilution cardiac output (TDCO) and the Fick Oxygen Principal (Fick). However both TDCO and Fick are highly invasive techniques that can cause infection and other complications, even in carefully controlled hospital environments. In TDCO, a pulmonary artery catheter (PAC), also known as a Swan-Ganz catheter, is typically inserted into the right portion of the patient's heart. Procedurally a bolus (typically 10 ml) of glucose or saline that is cooled to a known temperature is injected through the PAC. A temperature-measuring device within the PAC, located a known distance away (typically 6-10 cm) from where fluid is injected, measures the progressively increasing temperature of the diluted blood. CO is then estimated from a measured time-temperature curve, called the ‘thermodilution curve’. The larger the area under this curve, the lower the cardiac output. Likewise, a smaller the area under the curve implies a shorter transit time for the cold bolus to dissipate, hence a higher CO.
Fick involves calculating oxygen consumed and disseminated throughout the patient's blood over a given time period. An algorithm associated with the technique incorporates consumption of oxygen as measured with a spirometer with the difference in oxygen content of centralized blood measured from a PAC and oxygen content of peripheral arterial blood measured from an in-dwelling cannula.
Both TD and Fick typically measure CO with accuracies between about 0.5-1.0 l/min, or about +/−20% in the critically ill.
Several non-invasive techniques for measuring SV/CO/CP have been developed with the hope of curing the deficiencies of Fick and TD. For example, Doppler-based ultrasonic echo (Doppler/ultrasound) measures blood velocity using the well-known Doppler shift, and has shown reasonable accuracy compared to more invasive methods. But both two and three-dimensional versions of this technique require a specially trained human operator, and are thus, with the exception of the esophageal Doppler technique, impractical for continuous measurements. CO/SV can also be measured with techniques that rely on electrodes placed on the patient's torso that inject and then collect a low-amperage, high-frequency modulated electrical current. These techniques, based on electrical bioimpedance and called ‘impedance cardiography’ (ICG), ‘electrical cardiometry velocimetry’ (ECV), and ‘bioreactance’ (BR), measure a time-dependent electrical waveform that is modulated by the flow of blood through the patient's thorax. Blood is a good electrical conductor, and when pumped by the heart can further modulate the current injected by these techniques in a manner sensitive to the patient's CO. During a measurement, ICG, ECV, and BR each extract properties called left ventricular ejection time (LVET) and pre-injection period (PEP) from time-dependent ICG and ECG waveforms. A processer then analyzes the waveform with an empirical mathematical equation, shown below in Eq. 2, to estimate SV. CO is then determined from the product of SV and HR, as described above in Eq. 1.
ICG, ECV, and BR all represent a continuous, non-invasive alternative for measuring CO/SV, and in theory can be conducted with an inexpensive system and no specially trained operator. But the medical community has not embraced such methods, despite the fact that clinical studies have shown them to be effective with some patient populations. In 1992, for example, an analysis by Fuller et al. analyzed data from 75 published studies describing the correlation between ICG and TD/Fick (Fuller et al., The validity of cardiac output measurement by thoracic impedance: a meta-analysis; Clinical Investigative Medicine; 15: 103-112 (1992)). The study concluded using a meta analysis wherein, in 28 of these studies, ICG displayed a correlation of between r=0.80-0.83 against TDCO, dye dilution and Fick CO. Patients classified as critically ill, e.g. those suffering from acute myocardial infarction, sepsis, and excessive lung fluids, yielded worse results. Further impeding commercial acceptance of these techniques is the tendency of ICG monitors to be relatively bulky and similar in both size and complexity to conventional vital signs monitors. This means two large and expensive pieces of monitoring equipment may need to be located bedside in order to monitor a patient's vital signs and CO/SV. For this and other reasons, impedance-based measurements of CO have not achieved widespread commercial success.
ICG-based methodologies for measuring CO/SV have evolved since Fuller's analysis. For example, it has recently been shown that the dimensionless peak rate of change of the trans-thoracic electrical impedance pulse variation, which is defined as the maximum value of the derivative of the ICG waveform (dZ/dt)max divided by the base impedance (Zo), is an acceleration analog (with units of 1/s2). When subjected to square root transformation this yields ohmic mean velocity [(dZ/dt)max/Zo)]0.5. These parameters are described in detail in U.S. Pat. Nos. 7,740,590 and 7,261,697, the contents of which are fully incorporated herein by reference. Reasonable facsimiles of SV can be obtained when this value is multiplied by LVET and a volume conductor (Vc) allometrically related by body mass to the intrathoracic blood volume. As compared to CO measured with TDCO and transesophageal echocardiography, good to high correlation and limits of agreement within +/−30% are reported.
While most ICG measurements are conducted on the thorax, there is good evidence in the literature implying that left ventricular SV can be obtained from the upper extremity, and specifically the brachium. For example, Chemla et al. showed that peak aortic blood acceleration is highly correlated with peak brachial artery blood acceleration (r=0.79) (see, e.g., Chemla et al., Blood flow acceleration in the carotid and brachial arteries of healthy volunteers: respective contributions of cardiac performance and local resistance; Fundam Clin Pharmacol; 10: 393-399 (1996)). This study also demonstrated that, while brachial blood velocity is affected by downstream vasoactivity, peak brachial blood acceleration is solely affected by the upstream β-adrenergic influences of cardiac impulse formation. This suggests that square root transformation of brachial (dZ/dt)max/Zo may yield accurate estimations of SV when multiplied by LVET and a Vc of appropriate magnitude. Stanley et al. showed that the maximum early systolic upslope of the transthoracic and brachial impedance changes (ΔZ) are identical, indicating that they are linearly correlated (see, e.g. Stanley et al., Multi-channel electrical bioimpedance: a new noninvasive method to simultaneously measure cardiac and peripheral blood flow; J Clin Monit Comput; 21: 345-51 (2007)). This implies that, despite being of different magnitudes, the peak rate of change of the trans-thoracic and trans-brachial impedance changes can both be used to calculate SV. Finally, Wang et al. demonstrated that impedance changes (ΔZ(t)) in the forearm are highly correlated with Doppler-derived SV, showing a correlation coefficient of r=0.86 (see, e.g. Wang et al., Evaluation of changes in cardiac output from the electrical impedance waveform in the forearm; Physiol Meas; 28: 989-999 (2007)).
CO/SV can also be estimated from a time-dependent arterial blood pressure waveform measured, e.g., with a tonometer or in-dwelling arterial catheter. Algorithms can be used to extract pulse pressure (PP) and other contour-related features from these waveforms, which are then processed to estimate CO/SV. Unfortunately both the heart and its associated vessels can function independently and sometimes paradoxically, so changes in parameters like PP may both reflect and mask changes in CO/SV. In other words, measurements of CO using time-dependent arterial waveforms represent a combination of cardiac and vascular function.
Pulse arrival time (PAT), defined as the transit time for a pressure pulse launched by a heartbeat in a patient's arterial system, has been shown in a number of studies to correlate to both systolic (SYS) and diastolic (DIA) blood pressures. In these studies, PAT is typically measured with a conventional vital signs monitor that includes separate modules to determine both an electrocardiogram (ECG) and a value for pulse oximetry (SpO2). During a PAT measurement, multiple electrodes typically attach to a patient's chest to determine a time-dependent component of the ECG waveform characterized by a sharp spike called the ‘QRS complex’. The QRS complex indicates an initial depolarization of ventricles within the heart and, informally, marks the beginning of the heartbeat and a pressure pulse that follows. SpO2 is typically measured with a bandage or clothespin-shaped sensor that attaches to a patient's finger, and includes optical systems operating in both red and infrared spectral regions. A photodetector measures radiation emitted from the optical systems that transmits through the patient's finger. Other body sites, e.g., the ear, forehead, and nose, can also be used in place of the finger. During a measurement, a microprocessor analyses both red and infrared radiation measured by the photodetector to determine time-dependent waveforms corresponding to the different wavelengths, each called a photoplethysmogram waveform (PPG). From these a SpO2 value is calculated Time-dependent features of the PPG waveform indicate both pulse rate and a volumetric absorbance change in an underlying artery (e.g., in the finger) caused by the propagating pressure pulse.
Typical PAT measurements determine the time separating a maximum point on the QRS complex (indicating the peak of ventricular depolarization) and a portion of the PPG waveform (indicating the arrival of the pressure pulse). PAT depends primarily on arterial compliance, the propagation distance of the pressure pulse (which is closely approximated by the patient's arm length), and blood pressure. To account for patient-specific properties, such as arterial compliance, PAT-based measurements of blood pressure are typically ‘calibrated’ using a conventional blood pressure cuff. Typically during the calibration process the blood pressure cuff is applied to the patient, used to make one or more blood pressure measurements, and then removed. Going forward, the calibration measurements are used, along with a change in PAT, to determine the patient's blood pressure and blood pressure variability. PAT typically relates inversely to blood pressure, i.e., a decrease in PAT indicates an increase in blood pressure.
A number of issued U.S. Patents describe the relationship between PAT and blood pressure. For example, U.S. Pat. Nos. 5,316,008; 5,857,975; 5,865,755; and 5,649,543 each describe an apparatus that includes conventional sensors that measure ECG and PPG waveforms, which are then processed to determine PAT.
The invention provides a small-scale, body-worn monitor for measuring SV/CO/CP, along with cNIBP, HR, respiratory rate (RR), SpO2, and body temperature (TEMP), motion, and posture. Measurements of CO/SV are based on a measurement technique called ‘transbrachial electro-velocimetry’ (TBEV), which is described in detail below. TBEV measurements yield two time-dependent waveforms: Zo, which represents a base impedance in the brachial region, and is sensitive to slowly varying properties such as blood volume; and ΔZ(t), which features heartbeat-induced pulses that vary in contour as blood flows through the brachium during both systole and diastole. These waveforms are measured from the patient's brachium, a region that is somewhat immune to pulmonary ventilatory affects that can complicate conventional ICG measurements obtained from the thorax. Collectively, an algorithm running on a microprocessor within the body-worn monitor analyzes features analysis of both Zo and ΔZ(t) to determine values for each TBEV measurement. More specifically, to determine SV/CO/CP values, the monitor relies on a ‘hybrid measurement’ that collectively processes combinations of time-dependent PPG, ECG, and TBEV waveforms, along with physiological parameters (e.g. blood pressure values) extracted from these waveforms, measured by the body-worn monitor. From these waveforms parameters such as LVET and PEP can be estimated and used in a mathematical relationship to continuously and accurately estimate SV/CO/CP values, as described in detail below. Once determined, they are combined with conventional vital signs, and wirelessly transmitted by the body-worn monitor to a central station to effectively monitor the patient.
The TBEV waveform is measured with a small module that connects to a first set of adhesive electrodes worn in the patient's clavicle/brachial (CB) region. This region roughly extends from areas near the tip of the shoulder (proximal to the axilla) to the elbow (proximal to the antecubital fossa). ECG waveforms are measured with a small module that connects to second set of adhesive electrodes that are typically worn on the patient's thorax in a conventional Einthoven's triangle configuration. Both the TBEV and ECG modules also include a 3-axis accelerometer that measures acceleration waveforms (ACC) that are sensitive to motion. Both accelerometers measure, for example, breathing-induced chest wall excursions that can be processed to estimate RR, as well as larger scale motion that can be processed to determine motion-related properties such as activity level, posture, degree/magnitude of motion, and frequency of motion.
During a measurement, the TBEV and ECG modules transmit waveforms and numerical information through either a wired or wireless connection to a wrist-worn transceiver. The transceiver also connects to an optical sensor, worn on the patient's thumb, that measures PPG waveforms generated with optical systems featuring red (˜600 nm) and infrared (˜900 nm) light-emitting diodes (LEDs). These waveforms can be processed to determine values of SpO2. The wrist-worn transceiver also includes an internal accelerometer that measures ACC waveforms associated with hand motions. Both PPG waveforms, along with the ECG waveforms, can be processed to determine cNIBP values.
The TBEV component of the hybrid measurement is measured by injecting a high-frequency, low-amperage alternating current (AC) field along the course of the brachial artery in the CB region, followed by simultaneously sensing and signal processing voltage changes produced within the current field. The fundamental rational for TBEV derives from the direct proportionality and high correlation observed between peak ascending aortic and peak brachial artery blood flow acceleration. This technique is in diametric opposition to the generally accepted volumetric theory, an alternative approach that suggests it is the velocity-induced peak rate of change in the specific resistance of axially-directed flowing blood that causes a time-dependent change in the measured impedance. Computationally, TBEV-determined SV is obtained by taking the square root of the peak rate of change of electrical impedance pulse variation divided by the base impedance as measured in the CB region, i.e. [(dZ/dt)max/Zo]0.5. This parameter is then multiplied by LVET and a constant Vc to yield SV.
The body-worn monitor simultaneously provides a technique for measuring cNIBP, based on either PAT, pulse transit time (PTT) or vascular transit time (VTT), as described in more detail in the above-referenced patent applications. These documents describe cNIBP measurements made using the ‘Composite Method’, described in detail below, which features a number of improvements over conventional PAT and PTT measurements.
Upon completion of a measurement, the body-worn monitor wirelessly transmits waveforms, vital signs, and SV/CO/CP values to a remote monitor, such as a personal computer (PC), workstation at a nursing station, tablet computer, personal digital assistant (PDA), or cellular telephone. Typically the wireless transmitter is within the wrist-worn transceiver, which also displays and further analyzes this information. Both the remote monitor and the wrist-worn transceiver can additionally include a barcode scanner, touch screen display, camera, voice and speaker system, and wireless systems that operate with both local-area networks (e.g. 802.11 or ‘WiFi’ networks) and wide-area networks (e.g. the Sprint network).
In one aspect, for example, the invention provides a system for measuring both SV and CO from a patient. The system features an impedance sensor, connected to at least two patient-worn electrodes, and featuring an impedance circuit that processes signals from the at least two electrodes to measure an impedance signal from the patient. An optical sensor within the system connects to an optical probe, and features an optical circuit that measures at least one optical signal from the patient. A body-worn processing system operably connects to both the impedance sensor and the optical sensor and receives and processes the impedance signal to determine a first value of SV and CO. It then receives the optical signal and processes it to determine a second value of these parameters. Finally, the processing system collectively processes both the first and second values of SV and CO to determine a third value of these parameters, which it then reports to a display device.
In another aspect, the invention provides a similar system that also features an ECG sensor, connected to at least two body-worn electrodes, and featuring an ECG circuit. The ECG circuit is configured to process signals from the electrodes to measure an ECG waveform and HR value. A processing system connects to the impedance, optical, and ECG sensors, and receives time-dependent waveforms from each of these systems. It then collectively processes the ECG and optical signals to determine a blood pressure value, and then processes the blood pressure value to estimate SV and CO.
In another aspect, the invention provides a similar system that features ECG, impedance, and optical sensors. Collectively these sensors generate signals that are processed to determine a collection of SV ‘estimators’. The various estimators are then processed with a variety of algorithms to estimate stroke volume.
In another aspect the invention provides a method for determining SV that features the following steps: (a) measuring an impedance signal with an impedance sensor operably connected to the body-worn monitor; (b) measuring an optical signal with an optical sensor; (c) processing the impedance signal to determine a value of (dZ/dt)max; (d) processing the optical signal to determine a value of SFT; and (e) collectively processing Z0, (dZ/dt)max and SFT to determine the SV.
In another aspect, the invention provides a method of determining cardiac power, which as described in detail below is the product of CO and MAP. Here, CO is determined by processing an ECG waveform to determine a heart rate value, and a combination of impedance and optical waveforms to determine SV. MAP is then calculated from a PAT value determined from PPG and ECG waveforms. Alternatively, MAP is calculated from a VTT value determined from TBEV and PPG waveforms. In both cases, the Composite Method processes either PAT or PTT to determine MAP.
In another aspect, the invention provides a body-worn system for measuring a SV value from a patient. The body-worn system features a TBEV module that includes an electrical circuit configured to inject a current proximal to the patient's brachium. The circuit's bottom portion includes a pair of electrical connectors that are configured to snap into a pair of mated connectors disposed on a first electrode, with a first connector configured to inject the current into a first portion of the electrode, and a second connector configured to measure signals from a second portion of the electrode that relate to a voltage. An analog circuit then processes the signals from the second connector to generate a voltage value. A processor interfaced to the analog circuit converts the voltage value, or a value calculated therefrom, into a time-dependent resistance value, and then converts the time-dependent resistance value into a SV value.
In yet another aspect, the invention provides a method for determining SV from a patient that includes the following steps: (a) measuring a motion waveform with a first motion sensor; (b) processing the motion waveform with a motion algorithm to determine a motion-related parameter; (c) comparing the motion-related parameter to a pre-determined threshold parameter to determine if the patient's motion exceeds an acceptable level; (d) measuring an impedance waveform from the patient with an impedance sensor if the patient's motion does not exceed an acceptable level; and (e) calculating a SV value from the impedance waveform if the patient's motion does not exceed an acceptable level.
The Composite Method for cNIBP is described in detail in the following patent application, the contents of which are fully incorporated herein by reference: BODY-WORN SYSTEM FOR MEASURING CONTINUOUS NON-INVASIVE BLOOD PRESSURE (cNIBP), U.S. Ser. No. 12/650,354, filed Nov. 15, 2009. It includes both pressure-dependent and pressure-free measurements, and is based on the discovery that PAT and the PPG waveform used to determine it are strongly modulated by an applied pressure. During a pressure-dependent measurement, also referred to herein as an ‘indexing measurement’, two events occur as the pressure gradually increases to the patient's systolic pressure: 1) PAT increases, typically in a non-linear manner, once the applied pressure exceeds diastolic pressure; and 2) the magnitude of the PPG's amplitude systematically decreases, typically in a linear manner, as the applied pressure approaches systolic pressure. The applied pressure gradually decreases blood flow and consequent blood pressure in the patient's arm, and therefore induces the pressure-dependent increase in PAT. Each of the resulting pairs of PAT/blood pressure readings measured during the period of applied pressure can be used as a calibration point. Moreover, when the applied pressure equals SYS, the amplitude of the PPG waveform is completely eliminated, and PAT is no longer measurable. Collectively analyzing both PAT and the PPG waveform's amplitude over a suitable range, along with the pressure waveform using techniques borrowed from conventional oscillometry, yields the patient's SYS, DIA, and MAP, along with a patient-specific slope relating PAT and MAP. From these parameters the patient's cNIBP can be determined without using a conventional cuff.
A combination of several algorithmic features improves the efficacy of the Composite Method over conventional PAT measurements of cNIBP. For example, sophisticated, real-time digital filtering removes high-frequency noise from the PPG waveform, allowing its onset point to be accurately detected. When processed along with the ECG waveform, this ensures measurement of an accurate PAT and, ultimately, cNIBP value. The pressure-dependent indexing method, which is made during inflation of the arm-worn cuff, yields multiple data points relating PAT and blood pressure during a short (˜60 second) measurement. Processing of these data points yields an accurate patient-specific slope relating PAT to cNIBP. Inclusion of multiple accelerometers yields a variety of signals that can determine features like arm height, motion, activity level, and posture that can be further processed to improve accuracy of the cNIBP calculation, and additionally allow it to be performed in the presence of motion artifacts. And a model based on femoral blood pressure, which is more representative of pressure in the patient's core, can reduce effects such as ‘pulse pressure amplification’ that can elevate blood pressure measured at a patient's extremities.
The Composite Method can also include an ‘intermediate’ pressure-dependent measurement wherein the cuff is partially inflated. This partially decreases the amplitude of the PPG waveform in a time-dependent manner. The amplitude's pressure-dependent decrease can then be ‘fit’ with a numerical function to estimate the pressure at which the amplitude completely disappears, indicating systolic pressure.
For the pressure-dependent measurement, a small pneumatic system attached to the cuff inflates the bladder to apply pressure to an underlying artery according to the pressure waveform. The cuff is typically located on the patient's upper arm, proximal to the brachial artery, and time-dependent pressure is measured by an internal pressure sensor, such as an in-line Wheatstone bridge or strain gauge, within the pneumatic system. The pressure waveform gradually ramps up in a mostly linear manner during inflation, and then slowly rapidly deflates through a ‘bleeder valve’ during deflation. During inflation, mechanical pulsations corresponding to the patient's heartbeats couple into the bladder as the applied pressure approaches DIA. The mechanical pulsations modulate the pressure waveform so that it includes a series of time-dependent oscillations. The oscillations are similar to those measured with an automated blood pressure cuff using oscillometry, only they are measured during inflation rather than deflation. They are processed as described below to determine a ‘processed pressure waveform’, from which MAP is determined directly, and SYS and DIA are determined indirectly.
Pressure-dependent measurements performed on inflation have several advantages to similar measurements performed on deflation, which are convention. For example, inflation-based measurements are relatively fast and comfortable compared to those made on deflation. Most conventional cuff-based systems using deflation-based oscillometry take roughly four times longer than the Composite Method's pressure-dependent measurement. Inflation-based measurements are possible because of the Composite Method's relatively slow inflation speed (typically 5-10 mmHg/second) and high sensitivity of the pressure sensor used within the body-worn monitor. Moreover, measurements made during inflation can be immediately terminated once systolic blood pressure is calculated. In contrast, conventional cuff-based measurements made during deflation typically apply a pressure that far exceeds the patient's systolic blood pressure; pressure within the cuff then slowly bleeds down below DIA to complete the measurement.
Pressure-free measurements immediately follow the pressure-dependent measurements, and are typically made by determining PAT with the same optical and electrical sensors used in the pressure-dependent measurements. Specifically, the body-worn monitor processes PAT and other properties of the PPG waveform, along with the patient-specific slope and measurements of SYS, DIA, and MAP made during the pressure-dependent measurement, to determine cNIBP.
The invention in general, and particularly the hybrid measurement for SV/CO/CP, features many advantages over conventional techniques used to measure these properties. Compared to TDCO and Fick, for example, the body-worn monitor facilitates continuous, noninvasive measurement of these values that is highly accurate and has a low-risk of detrimental complications, such as infection and pulmonary artery vessel perforation. And unlike measurements based on TDCO, Fick, and Doppler, the hybrid measurement does not require a specially trained observer. TBEV measurements are performed at the brachium, which by itself has several advantages over conventional ICG measurements made from the thorax. For example, complications in the pulmonary system, i.e. intra-thoracic liquids and pulmonary edema, do not affect SV/CO/CP values measured from this region. Similarly, the baseline trans-brachial quasi-static impedance, Zo is not affected by medical equipment sometimes present in the thorax, such as chest tubes, external pacemaker wires, and central venous lines. Typically thoracic ICG measurements require 8 separate electrodes, whereas the TBEV measurement described herein only requires 2 separate electrodes. Finally, without the influence of pulmonary ventilation and pulmonary artery pulsations, the signal-to-noise ratio of waveforms measured from the brachium is relatively high.
These and other advantages of the invention will be apparent for the following detailed description, and from the claims.
Measurement Overview
Referring to
TBEV is a variation of conventional bioimpedance techniques, such as ICG, and measures waveforms from the CB region to determine time-dependent parameters such as systolic flow time (SFT), (dZ/dt)max, and Zo. These parameters feed into Eq. 3, below, where they are coupled with a static parameter Vc to determine SV.
Here, SV is obtained by taking the square root of the peak rate of change of each TBEV pulse divided by the transbrachial base impedance, Zo. This parameter is then multiplied by SFT and a constant-magnitude Vc to yield SV. The derivation of Eq. 3 is described in detail in U.S. Pat. No. 6,511,438 and in the following reference, the contents of which are fully incorporated herein by reference: Bernstein et al., Stroke Volume Obtained By Electrical Interrogation of the Brachial Artery: Transbrachial Electrical Bioimpedance Velocimetry. Unpublished manuscript, submitted 2012. Eq. 3 assumes that (dZ/dt)max/Zo represents a dimensionless acceleration of blood (with units of 1/s2), which is the ohmic analog of peak aortic blood acceleration (cm/s2). Forceful systolic ejection of blood from the left ventricle of the heart aligns the erythrocytes in parallel during systolic flow to generate a pulsatile increase in conductivity. For this model, Vc is estimated entirely from weight, and is independent of any factors that depend on electrode separation.
Along with SV, the body-worn monitor simultaneously measures cNIBP values (SYS, DIA, MAP, and PP) using a cuffless technique called the ‘Composite Method’, which is described in detail above. According to the hybrid method, SV is determined explicitly from the TBEV waveforms, and can be estimated from the cNIBP values. From these parameters multiple estimators 1-7 are determined which the algorithm 10 collectively processes to determine SV. Additionally, the body-worn monitor features multiple accelerometers that generate time-dependent ACC waveforms, which are then further processed by a motion algorithm 11 to estimate the patient's level of motion. A function for filtering and rejecting bad data 8 processes information from both the estimators 1-7 and the motion algorithm 11 to determine a collection of valid data points, which are then linearly combined with another function 9 to determine final values of SV. The valid data points, for example, are relatively uncorrupted by motion artifacts; they are determined when the motion algorithm 11 compares a parameter extracted from an ACC waveform to a pre-determined ‘motion threshold’ value. If the parameter exceeds the pre-determined threshold value, the function 8 rejects the corresponding SV values. On the other hand, if the parameter is lower than the pre-determined threshold value, the function 8 approves the corresponding SV value, and it is passed into the linear combination algorithm 9, where it will be processed to determine a final value for SV.
Different threshold values can be applied for SV calculated from TBEV 7, a measurement that is particularly sensitive to motion, and SV estimated from estimators related to blood pressure 1-6, which are less sensitive to motion. For example, the motion algorithm 11 may determine that a small amount of motion is present, and thus the linear combination algorithm 9 relies completely on SV values determined from the estimators related to blood pressure 1-6. Or it may determine that a large amount of motion is present, and in response the linear combination algorithm 9 will not report an SV value. If the motion algorithm 11 determines that no motion is present, the linear combination algorithm 9 typically reports a SV value determined entirely from TBEV 7.
In embodiments, the linear combination algorithm 9 combines different estimators using a simple average or weighted average to determine a single value of SV. More sophisticated approaches can also be used to process the estimators. For example, specific estimators can be selected based on a patient's physiological condition or biometric parameters, e.g. their age, gender, weight, or height.
Once SV is determined, it can be further processed as defined in Eqs. 1, 2 to determine both CO and CP.
Upon completion of a measurement, the body-worn monitor wirelessly transmits SV/CO/CP values, along with conventional vital signs, to a remote processing system. For example, these data may flow through a hospital-based wireless network to a central computer interfaced to an electronic medical records system. From there, medical professionals, such as doctors, nurses, and first responders, can evaluate a constellation of physiological values corresponding to the patient to make a diagnosis. Typically, patients wear the body-worn monitor as they transition from the ambulance, into the hospital, and ultimately to the home.
A TBEV measurement, described in detail below, injects a low-amperage, high-frequency current into the patient's CB region, and monitors a voltage which relates to the time-dependent resistance encountered by the current through Ohm's Law (V=I×R). It is based on the assumption that the brachial artery, which is the only major artery in the CB region, undergoes little volumetric expansion during systole, and thus changes in resistance are due exclusively to acceleration-induced alignment of erythrocytes within this artery. Stated another way, as blood flows through the artery with each heartbeat, the diameter of the brachial artery stays relatively constant, but acceleration of the blood causes the erythrocytes to align. This physiological process consequently increases conductivity, and decreases resistance, in the artery. The time-dependent resistance in the artery is manifested as a first waveform, called ΔZ(t), which features a series of pulses, each corresponding to a unique heartbeat. A second TBEV waveform, Zo, is filtered to only reflect the baseline impedance of the artery, and is sensitive to relatively low-frequency processes, such as blood volume, interstitial fluids, and occasionally respiration rate.
Estimators for determining SV from blood pressure values include the Lilijestrand 1, Wesseling 2, MAP 3, and Herd 4 estimators. These depend linearly on blood pressure values, and are shown below in Table 1. In this table SYSarea refers to the area under the PPG waveform during systole, ΔPn is the beat-to-beat blood pressure change, is the duration of the cardiac cycle, and τn is a time constant that governs the intra-cycle dynamics of the Windkessel model.
These estimators are summarized in detail in the following reference, among other places, the contents of which are incorporated herein by reference: Chen, Cardiac Output Estimation from Arterial Blood Pressure Waveforms using the MIMIC II Database; Thesis for Masters Degree submitted to the Massachusetts Institute of Technology; (2009); and Parlikar et al., Model-Based Estimation of Cardiac Output and Total Peripheral Resistance, Computers in Cardiology 34: 379-382 (2007). Estimators based on blood pressure can be determined using the Composite Method, or alternatively with a conventional cuff-based method, such as oscillometry or auscultation.
Other SV estimators that can be processed by the algorithm include those based on PAT 5, which is determined using PPG and ECG waveforms measured by the body-worn monitor, and is described in the following reference, the contents of which are incorporated by reference: Wang et al., The non-invasive and continuous estimation of cardiac output using a photoplethysmogram and electrocardiogram during incremental exercise; Physiol. Meas.; 31: 715-726 (2010).
CO=D×[C−ln(PAT)]×(1−IPA)×(1+IPA)−1 (4)
In the equation D and C are constants defined in the Wang reference, and IPA is shown schematically in
The PPG waveform, taken by itself, can be analyzed and used as an ‘other’ estimator 6 for algorithm 10. This waveform represents a time-dependent volumetric expansion of the underlying artery from which it is measured, and is thus different than a traditional cNIBP waveform, such as that measured using an in-dwelling arterial catheter, which represents the time-dependent pressure in the artery. However, PPG and cNIBP waveforms share a similar morphology, particularly over relatively long time periods, and can be analyzed to estimate both blood flow dynamics and hence SV. The following reference, the contents of which are incorporated herein by reference, describes an analysis method for processing waveforms to extract these parameters: Lu et al., Continuous cardiac output monitoring in humans by invasive and noninvasive peripheral blood pressure waveform analysis; J Appl Physiol 101: 598-608 (2006).
In still other embodiments, an ‘other’ estimator 6 for the SV/CO/CP measurement can be based on a measurement technique performed by an external sensor that connects to the body-worn monitor. Such a connection can be made using either wired or wireless means. For example, a technique such as near-infrared spectroscopy (NIRS) can be used to estimate SV as described in the following references, the contents of which are incorporated herein by reference: Soller et al., Noninvasively determined muscle oxygen saturation is an early indicator of central hypovolemia in humans; J Appl Physiol 104: 475-481 (2008). A sensor incorporating a NIRS measurement can thus be integrated with the body-worn monitor and attached to the patient's body during a measurement. Values for SV calculated with this sensor are sent to the monitor through the wired or wireless connection, and can be incorporated in the algorithm 10 to further improve the accuracy of the continuous, non-invasive determination of SV. In all cases, the collection of estimators 1-6 relate to CO through a calibration factor (k in Table 1) determined from an absolute measurement of SV, which in the algorithm 10 is provided by the TBEV measurement 7. Typically TBEV determines SV to within about +/−20%. Perhaps more importantly, the estimators 1-6 and TBEV measurement 7 determine SV with completely different methodologies and from different locations on the body. Thus it is possible that combining the measurements into a single algorithm 10 may reduce error caused by well-known physiological effects that are typically isolated to these locations.
As shown in
Each conductive region 45 and 47 typically consists of a conductive ‘liquid gel’ material that roughly matches the impedance properties of human skin. The liquid gel is deposited on top of a conductive substrate coated with a large-area Ag:AgCl film that, in turn, is deposited on top of a flexible substrate 23. The liquid gel, for example, can be a sponge-like material saturated with a conductive gel or fluid. The neighboring conductive regions 45, 47 are electrically isolated from each other, and individually connect through a pair of individual conductive traces 54A, B to a pair of electrical leads 52A, B adhered to the flexible substrate 23. The electrical leads 52A, B, for example, can be metal rivets or posts that easily snap into a corresponding female connector. An insulating adhesive layer (not shown in the figure) dispersed between the conductive regions 45 and 47 electrically isolates these portions of the electrode 24, and is coated with an adhesive that enables it to be securely attached to the patient during a measurement.
Importantly, previous studies have indicated a strong correlation between peak blood acceleration in the aorta, where the SV is first manifested, and peak blood acceleration in the brachium, where TBEV measures a signal used to estimate SV by square root transformation. Insofar as velocities are concerned, peak aortic blood velocity is roughly 80-124 cm/s (mean ˜100 cm/s), while that in the brachial artery is roughly 30-70 cm/s (mean ˜50 cm/s). Experiments that measured these parameters are described in the following references, the contents of which are incorporated herein by reference: Gardin J M et al., Evaluation of blood flow velocity in the ascending aorta and main pulmonary artery of normal subjects by Doppler echocardiograpy. Am. Heart J. 1984; 107:310; Wilson S et al., Normal intracardiac and great artery blood velocity measurements by pulsed Doppler echocardiography. Br. Heart J. 1985; 53:451; Fronek A., Non invasive diagnostics in vascular disease. McGraw-Hill, N.Y. 1989, pp 117; Green D, et al., Assessment of brachial artery blood flow across the cardiac cycle: retrograde flows during bicycle ergometry. J. Appl. Physiol 2002; 93:361. These references indicate that, to a first approximation, the average blood velocity in the aorta is roughly twice that in the brachial artery.
In Eq. 5 δ represents compensation for body mass index, Zo is the base impedance, and L is estimated from the distance separating the current-injecting and voltage-measuring electrodes on the thorax. This equation and several mathematical derivatives are described in detail in the following reference, the contents of which are incorporated herein by reference: Bernstein, Impedance cardiography: Pulsatile blood flow and the biophysical and electrodynamic basis for the stroke volume equations; J Electr Bioimp; 1: 2-17 (2010). Eq. 5 depends on LVET, which is estimated from each pulse in the ICG waveform, as is described in more detail below. Both the Sramek-Bernstein Equation and an earlier derivative of this, called the Kubicek Equation, feature a ‘static component’, Z0, and a ‘dynamic component’, ΔZ(t), which relates to LVET and a (dZ/dt)max/Zo value, calculated from the derivative of the raw ICG signal, ΔZ(t). These equations assume that (dZ/dt)max/Zo represents a radial velocity (with units of Ω/s) of blood due to volume expansion of the aorta.
In contrast to the aorta, the brachial artery is a relatively muscular vessel that undergoes little expansion during systole 68A and recoil during diastole 68B; its arterial volume, as shown in
Sensor Configurations
Referring to
An ECG module 40 worn on the patient's thorax connects to the TBEV module 22 through a similar cable 27 that only includes conductors for transmitting digital signals according to the CAN protocol. The ECG module 40 connects to a trio of disposable ECG electrodes 42A-C, disposed on the patient's thorax in a conventional ‘Einthoven's triangle’ configuration, through a corresponding trio of ECG leads 44A-C. During a measurement, the ECG module 40 measures analog signals from each electrode 42A-C and lead 44A-C, and performs a differential amplification of these signals according to known techniques in the art to generate an ECG waveform. An analog-to-digital converter (not shown in the figure) digitizes the ECG waveform, and a microprocessor (also not shown in the figure) analyzes the well-known QRS complex within this waveform with a beat-picking algorithm to determine a HR value. Digital representations of these data are sent within CAN-formatted packets through the cable 27 to a CAN transceiver (not shown in the figure) within the TBEV module 22. There, the packets are combined with corresponding packets that include the TBEV waveform and SV values, which are calculated as described above. These packets pass through CAN conductors in the cable 26, past the second two-part electrode 28, and then through a third cable 29 to a wrist-worn transceiver 30 that connects to the patient's wrist using a plastic cradle 32 and Velcro strap 34. These components are described in more detail in the following co-pending patent applications, the contents of which have been previously incorporated by reference: BODY-WORN VITAL SIGN MONITOR, U.S. Ser. No. 12/560,077, filed Sep. 15, 2009; and BODY-WORN VITAL SIGN MONITOR, U.S. Ser. No. 12/762,726, filed Apr. 19, 2009. The wrist-worn transceiver 30 additionally connects through a short cable 38 that carries only analog signals measured by a thumb-worn optical sensor 36. Within the wrist-worn transceiver is a pulse oximetry circuit (not shown in the figure) that converts signals measured by the optical sensor 36 to generate PPG waveforms and corresponding values of SpO2. A microprocessor within the wrist-worn transceiver 30 processes PPG and ECG waveforms to generate a value of PAT, or alternatively TBEV and PPG waveforms to generate a value of VTT. These transit times are converted into cNIBP values using the Composite Method, as described above. The cNIBP values, in turn, are converted into SV estimators using the algorithm shown in
Technically, TBEV-based measurements of SV only require an isolated TBEV waveform, and can be performed without an ECG waveform. However, this signal, which is relatively easy to measure and denotes the beginning of the cardiac cycle associated with each heartbeat, can be used to ‘gate’ the relatively weak TBEV signal to make it easier to extract the properties described above in Eq. 3. More specifically, the above-described software beat picker can detect the QRS complex within the ECG waveform, which is associated with the onset of an individual heartbeat. Relevant portions of the TBEV waveform typically follow the QRS complex by a few hundred milliseconds. Analysis of these portions of the TBEV waveforms yields properties such as SFT, (dZ/dt)max, and Zo that are used to calculate SV as described above. Gating the TBEV waveform in this manner can be particularly effective in analyzing these properties when noise is present in the TBEV waveform, e.g. during periods of motion.
HR, determined from the ECG waveform, is used to convert SV into CO and CO into CP, as described in Eqs. 1 and 2, above. Typically HR is determined from the time period separating neighboring QRS complexes in the ECG waveform; alternatively it can be estimated from neighboring pulses in either the PPG or TBEV waveform.
The ECG module 40 can additionally connect to 5 leads, and alternatively 12 leads. It is typically hard-wired into the TBEV module 22. The third cable 29 plugs into the wrist-worn transceiver 30 using a detachable connector 31 that allows it to be easily removed. In other embodiments the order of the ECG module 40 and TBEV module 22 can be reversed so that the TBEV module 22 is closer to the thorax, and the ECG module 40 is closer to the CB region. In still other embodiments, the TBEV module 22 can be disposed on the third cable 29 and attach directly to the second electrode 28, and the ECG module 40 can be disposed in the original location of the TBEV module 22, and be encapsulated by a housing that attaches to the first electrode 24 and feeds analog signals into the TBEV module 22. In general, multiple configurations of the various modules, cables, and electrodes shown in
An alternate embodiment of the invention is shown in
The ECG module 40 is attached to a second electrode 24 with a geometry similar to that shown in
In
Within the body-worn monitor 19 are three three-axis accelerometers that measure ACC waveforms corresponding to x, y, and z-axes. The accelerometers, which are not shown in the figure, are disposed within the ECG module 40, the TBEV module 22, and the wrist-worn transceiver 30. During a measurement, ACC waveforms generated by the accelerometers are processed by microprocessors within each of the above-mentioned components to determine a motion-related parameter. Measurements of SV (or any vital sign, for that matter) are rejected if the parameter, or a secondary parameter derived therefrom, is lower than the pre-determined threshold value. Algorithms for such calculations are described, for example, in the following co-pending patent application, the contents of which are incorporated herein by reference: VITAL SIGN MONITORING SYSTEM FEATURING 3 ACCELEROMETERS, U.S. Ser. No. 12/469,094 (filed May 20, 2009).
Within the TBEV module is an analog circuit 100, shown in
The circuit 100 features a first electrode 105B that injects a high-frequency, low-amperage current (Iin) into the patient's brachium. This serves as the current source. Typically a current pump 102 provides the modulated current, with the modulation frequency typically being between 50-100 KHz, and the current magnitude being between 0.1 and 10 mA. Preferably the current pump 102 supplies current with a magnitude of 4 mA that is modulated at 70 kHz through the first electrode 105B. A second electrode 104A serves as the current drain (Iout).
A pair of electrodes 104B, 105A measure the time-dependent voltage encountered by the propagating current. These electrodes are indicated in the figure as V+ and V−. As described above, using Ohm's law (V=I×R), the measured voltage divided by the magnitude of the injected current yields a time-dependent resistance to ac (i.e. impedance) that relates to blood flow in the brachial artery. As shown by the waveform 128 in the figure, the time-dependent resistance features a slowly varying dc offset, characterized by Zo, that indicates the baseline impedance encountered by the injected current; for TBEV this will depend, for example, on the amount of fat, bone, muscle, and blood volume in the brachium of a given patient. Zo, which typically has a value between about 10 and 150Ω, is also influenced by low-frequency, time-dependent processes such as respiration. Such processes affect the inherent capacitance near the brachial region that TBEV measures, and are manifested in the waveform by low-frequency undulations, such as those shown in the waveform 128. A relatively small (typically 0.1-0.5Ω) ac component, ΔZ(t), lies on top of Zo and is attributed to changes in resistance caused by the heartbeat-induced blood that propagates in the brachial artery, as described in detail above. ΔZ(t) is processed with a high-pass filter to form a TBEV signal that features a collection of individual pulses 130 that are ultimately processed to ultimately determine stroke volume and cardiac output.
Voltage signals measured by the first electrode 104B (V+) and the second electrode 105A (V−) feed into a differential amplifier 107 to form a single, differential voltage signal which is modulated according to the modulation frequency (e.g. 70 kHz) of the current pump 102. From there, the signal flows to a demodulator 106, which also receives a carrier frequency from the current pump 102 to selectively extract signal components that only correspond to the TBEV measurement. The collective function of the differential amplifier 107 and demodulator 106 can be accomplished with many different circuits aimed at extracting weak signals, like the TBEV signal, from noise. For example, these components can be combined to form a ‘lock-in amplifier’ that selectively amplifies signal components occurring at a well-defined carrier frequency. Or the signal and carrier frequencies can be deconvoluted in much the same way as that used in conventional AM radio using a circuit features one or more diodes. The phase of the demodulated signal may also be adjusted with a phase-adjusting component 108 during the amplification process. In one embodiment, the ADS 1298 family of chipsets marketed by Texas Instruments may be used for this application. This chipset features fully integrated analog front ends for both ECG and impedance pneumography. The latter measurement is performed with components for digital differential amplification, demodulation, and phase adjustment, such as those used for the TBEV measurement, that are integrated directly into the chipset.
Once the TBEV signal is extracted, it flows to a series of analog filters 110, 112, 114 within the circuit 100 that remove extraneous noise from the Zo and ΔZ(t) signals. The first low-pass filter 1010 (30 Hz) removes any high-frequency noise components (e.g. power line components at 60 Hz) that may corrupt the signal. Part of this signal that passes through this filter 110, which represents Zo, is ported directly to a channel in an analog-to-digital converter 120. The remaining part of the signal feeds into a high-pass filter 112 (0.1 Hz) that passes high-frequency signal components responsible for the shape of individual TBEV pulses 130. This signal then passes through a final low-pass filter 114 (10 Hz) to further remove any high-frequency noise. Finally, the filtered signal passes through a programmable gain amplifier (PGA) 116, which, using a 1.65V reference, amplifies the resultant signal with a computer-controlled gain. The amplified signal represents ΔZ(t), and is ported to a separate channel of the analog-to-digital converter 120, where it is digitized alongside of Zo. The analog-to-digital converter and PGA are integrated directly into the ADS 1298 chipset described above. The chipset can simultaneously digitize waveforms such as Zo and ΔZ(t) with 24-bit resolution and sampling rates (e.g. 500 Hz) that are suitable for physiological waveforms. Thus, in theory, this one chipset can perform the function of the differential amplifier 107, demodulator 108, PGA 116, and analog-to-digital converter 120. Reliance of just a single chipset to perform these multiple functions ultimately reduces both size and power consumption of the TBEV circuit 100.
Digitized Zo and ΔZ(t) waveforms are received by a microprocessor 124 through a conventional digital interface, such as a SPI or I2C interface. Algorithms for converting the waveforms into actual measurements of SV and CO are performed by the microprocessor 124. The microprocessor 124 also receives digital motion-related waveforms from an on-board accelerometer 122, and processes these to determine parameters such as the degree/magnitude of motion, frequency of motion, posture, and activity level.
Wrist-Worn Transceiver
The wrist-worn transceiver 272 used to perform the hybrid measurement method of SV according to the invention is shown in more detail in
As described above, the wrist-worn transceiver attaches to the patient's wrist using a flexible strap 290 which threads through two D-ring openings in a plastic housing 206. The transceiver 272 features a touch panel display 220 that renders a GUI 273 that is altered depending on the viewer (typically the patient or a medical professional). Specifically, the transceiver 272 includes a small-scale infrared barcode scanner 202 that, during use, can scan a barcode worn on a badge of a medical professional. The barcode indicates to the transceiver's software that, for example, a nurse or doctor is viewing the user interface. In response, the GUI 273 displays vital sign data and other medical diagnostic information appropriate for medical professionals. Using this GUI 273, the nurse, doctor, or medical professional, for example, can view the vital sign information, set alarm parameters, and enter information about the patient (e.g. their demographic information, medication, or medical condition). For example, for the SV/CO/CP measurement described above, the clinician can enter the patient's gender, height, weight, and age. These parameters may be used in the calculations described in Eq. 3, above, to estimate the Vc used in Eq. 3 to calculate SV. Once entered, the clinician can press a button on the GUI 273 indicating that these operations are complete and that the appropriate data for the SV measurement has been entered. At this point, the display 220 renders an interface that is more appropriate to the patient, such as one that simply displays the time of day and battery power.
The transceiver 272 features three CAN connectors 204a-c on the side of its upper portion, each which supports the CAN protocol and wiring schematics, and relays digitized data to the transceiver's internal CPU. Digital signals that pass through the CAN connectors include a header that indicates the specific signal (e.g. TBEV, ECG, ACC, or numerical values calculated from these waveforms) and the sensor from which the signal originated. In alternative embodiments some of these are sent from the chest-worn module through Bluetooth, which maintains the CAN structure of the packets. This allows the CPU to easily interpret signals that arrive through the CAN connectors 204a-c, such as those described above corresponding to TBEB and ECG waveforms, and means that these connectors are not associated with a specific cable. Any cable connecting to the transceiver can be plugged into any connector 204a-c.
As shown in
The second CAN connector 204b receives the cable 295 that connects to another sensor, e.g. a pneumatic cuff-based system used to measure blood pressure values used in the Composite Method. This connector 204b receives a time-dependent pressure waveform delivered by the pneumatic system to the patient's arm, along with values for SYS, DIA, and MAP values determined during the Composite Method's indexing measurement. The cable 295 unplugs from the connector 204b once the indexing measurement is complete, and is plugged back in after approximately four hours for another indexing measurement.
The final CAN connector 204c can be used for an ancillary device, e.g. a glucometer, infusion pump, body-worn insulin pump, NIRS system, ventilator, or et-CO2 measurement system. As described above, digital information generated by these systems will include a header that indicates their origin so that the CPU can process them accordingly.
The transceiver 272 includes a speaker 201 that allows a medical professional to communicate with the patient using a voice over Internet protocol (VOIP). For example, using the speaker 201 the medical professional could query the patient from a central nursing station or mobile phone connected to a wireless, Internet-based network within the hospital. Or the medical professional could wear a separate transceiver similar to the shown in the figure, and use this as a communication device. In this application, the transceiver 272 worn by the patient functions much like a conventional cellular telephone or ‘walkie-talkie’: it can be used for voice communications with the medical professional and can additionally relay information describing the patient's vital signs and motion. The speaker can also enunciate pre-programmed messages to the patient, such as those used to calibrate the chest-worn accelerometers for a posture calculation, as described above.
Clinical Data from CO/SV Measurements
Determining SFT from the derivatized TBEV waveform shown in
LVET=−0.0017×HR+0.413 (6)
Weissler's Regression allows LVET, equivalent to SFT 94, 95, to be estimated from HR determined from either the ECG waveform, or alternatively from PR determined from the PPG waveform.
To further support this point,
More specifically, the figures show Doppler/ultrasound images indicating forward blood velocity is zero prior to systole (
Another advantage of TBEV waveforms compared to those measured with conventional ICG is that they undergo little patient-to-patient variation, thus making their computer-based analysis relatively easy.
The analysis described above was used in a formal clinical study to test accuracy of determining CO using TBEV and Eq. 3 above, compared to CO determined using Doppler/ultrasound. Correlation and Bland-Altman plots are shown, respectively, in
Measuring Respiration Rate with TBEV
TBEV, like techniques such as impedance pneumography, injects small amounts of current into the patient's body, and measures resistance (i.e. impedance) encountered by the current to calculate a parameter of interest. During a TBEV measurement, heartbeat-induced blood flow results in the pulsatile component of ΔZ(t). Additionally, changes in capacitance due to breathing may also affect the impedance as measured by TBEV.
The algorithm for calculating respiration rate can be expanded to include processing of signal from the accelerometer within the TBEV module. For example, as shown in
Measuring TBEV Waveforms in the Absence of Motion
In
Data shown in
Processing ACC Waveforms to Determine Posture
A patient's posture may influence their values of SV/CO/CP, and thus knowing this parameter may improve the measurement described herein. To make this measurement, the body-worn monitor described above includes three 3-axis accelerometers as well as the ECG and TBEV circuits. In addition to determining SV/CO/CP, these sensors can generate time-dependent waveforms that when analyzed yield RR and the patient's motion-related properties, e.g. degree of motion, posture, and activity level.
Specifically, torso posture is determined for a patient 410 using angles determined between the measured gravitational vector and the axes of a torso coordinate space 411. The axes of this space 411 are defined in a three-dimensional Euclidean space where {right arrow over (R)}CV is the vertical axis, {right arrow over (R)}CH is the horizontal axis, and {right arrow over (R)}CN is the normal axis. These axes must be identified relative to a ‘chest accelerometer coordinate space’ before the patient's posture can be determined.
The first step in determining a patient's posture is to identify alignment of {right arrow over (R)}CV in the chest accelerometer coordinate space. This can be determined in either of two approaches. In the first approach, {right arrow over (R)}CV is assumed based on a typical alignment of the body-worn monitor relative to the patient. During a manufacturing process, these parameters are then preprogrammed into firmware operating on the wrist-worn transceiver. In this procedure it is assumed that accelerometers within the body-worn monitor are applied to each patient with essentially the same configuration. In the second approach, {right arrow over (R)}CV is identified on a patient-specific basis. Here, an algorithm operating on the wrist-worn transceiver prompts the patient (using, e.g., video instruction operating on the wrist-worn transceiver, or audio instructions transmitted through a speaker) to assume a known position with respect to gravity (e.g., standing upright with arms pointed straight down). The algorithm then calculates {right arrow over (R)}CV from DC values corresponding to the x, y, and z-axes of the chest accelerometer while the patient is in this position. This case, however, still requires knowledge of which arm (left or right) the monitor is worn on, as the chest accelerometer coordinate space can be rotated by 180 degrees depending on this orientation. A medical professional applying the monitor can enter this information using the GUI, described above. This potential for dual-arm attachment requires a set of two pre-determined vertical and normal vectors that are interchangeable depending on the monitor's location. Instead of manually entering this information, the arm on which the monitor is worn can be easily determined following attachment using measured values from the chest accelerometer values, with the assumption that {right arrow over (R)}CV is not orthogonal to the gravity vector.
The second step in the procedure is to identify the alignment of {right arrow over (R)}CN in the chest accelerometer coordinate space. The monitor determines this vector in the same way it determines {right arrow over (R)}CV using one of two approaches. In the first approach the monitor assumes a typical alignment of the chest-worn accelerometer on the patient. In the second approach, the patient is prompted to the alignment procedure and asked to assume a known position with respect to gravity. The monitor then calculates {right arrow over (R)}CN from the DC values of the time-dependent ACC waveform.
The third step in the procedure is to identify the alignment of {right arrow over (R)}CH in the chest accelerometer coordinate space. This vector is typically determined from the vector cross product of {right arrow over (R)}CV and {right arrow over (R)}CN, or it can be assumed based on the typical alignment of the accelerometer on the patient, as described above.
A patient's posture is determined using the coordinate system described above and in
where the dot product of the two vectors is defined as:
{right arrow over (R)}G[n]·{right arrow over (R)}CV=(yCx[n]×rCVx)+(yCy[n]×rCVy)+(yCz[n]×rCVz) (8)
The definitions of the norms of {right arrow over (R)}G and {right arrow over (R)}CV are given by Eqs. 9 and 10:
∥{right arrow over (R)}G[n]∥=√{square root over ((yCx[n])2+(yCy[n])2+(yCz[n])2)} (9)
∥{right arrow over (R)}CV∥=√{square root over ((rCVx)2+(rCVy)2+(rCVz)2)} (10)
As indicated in Eq. 12, the monitor compares the vertical angle θVG to a threshold angle to determine whether the patient is vertical (i.e. standing upright) or lying down:
if θVG≦45° then Torso State=0, the patient is upright (11)
If the condition in Eq. 11 is met the patient is assumed to be upright, and their torso state, which is a numerical value equated to the patient's posture, is equal to 0. The patient is assumed to be lying down if θVG>45 degrees. Their lying position is then determined from angles separating the two remaining vectors, as defined below.
The angle θNG between {right arrow over (R)}CN and {right arrow over (R)}G determines if the patient is lying in the supine position (chest up), prone position (chest down), or on their side. Based on either an assumed orientation or a patient-specific calibration procedure, as described above, the alignment of {right arrow over (R)}CN is given by Eq. 11, where i, j, k represent the unit vectors of the x, y, and z axes of the chest accelerometer coordinate space respectively:
{right arrow over (R)}CN=rCNxî+rCNyĵ+rCNz{circumflex over (k)} (12)
The angle between {right arrow over (R)}CN and {right arrow over (R)}G determined from DC values extracted from the chest ACC waveform is given by Eq. 13:
The body-worn monitor determines the normal angle θNG and then compares it to a set of predetermined threshold angles to determine which position in which the patient is lying, as shown in Eq. 14:
if θNG≦35° then Torso State=1, the patient is supine
if θNG≧135° then Torso State=2, the patient is prone (14)
If the conditions in Eq. 14 are not met then the patient is assumed to be lying on their side. Whether they are lying on their right or left side is determined from the angle calculated between the horizontal torso vector and measured gravitational vectors, as described above.
The alignment of {right arrow over (R)}CH is determined using either an assumed orientation, or from the vector cross-product of {right arrow over (R)}CV and {right arrow over (R)}CN as given by Eq. 15, where i, j, k represent the unit vectors of the x, y, and z axes of the accelerometer coordinate space respectively. Note that the orientation of the calculated vector is dependent on the order of the vectors in the operation. The order below defines the horizontal axis as positive towards the right side of the patient's body.
{right arrow over (R)}CH=rCVxî+rCVyĵ+rCVz{circumflex over (k)}={right arrow over (R)}CV×{right arrow over (R)}CN (15)
The angle θHG between {right arrow over (R)}CH and {right arrow over (R)}G is determined using Eq. 16:
The monitor compares this angle to a set of predetermined threshold angles to determine if the patient is lying on their right or left side, as given by Eq. 17:
if θHG≧90° then Torso State=3, the patient is on their right side
if θNG<90° then Torso State=4, the patient is on their left side (17)
Table 1 describes each of the above-described postures, along with a corresponding numerical torso state used to render, e.g., a particular icon on a remote computer.
Data shown in
Other embodiments are within the scope of the invention. For example, the TBEV harness and its associated electrode can take on a variety of configurations. One of these is shown in
In addition to those methods described above, the body-worn monitor can use a number of additional methods to calculate blood pressure and other properties from the optical and electrical waveforms. These are described in the following co-pending patent applications, the contents of which are incorporated herein by reference: 1) CUFFLESS BLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS, INTERNET-BASED SYSTEM (U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2) CUFFLESS SYSTEM FOR MEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014; filed Apr. 7, 2004); 3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WEB SERVICES INTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004); 4) VITAL SIGN MONITOR FOR ATHLETIC APPLICATIONS (U.S. Ser. No.; filed Sep. 13, 2004); 5) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILE DEVICE (U.S. Ser. No. 10/967,511; filed Oct. 18, 2004); 6) BLOOD PRESSURE MONITORING DEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S. Ser. No. 10/967,610; filed Oct. 18, 2004); 7) PERSONAL COMPUTER-BASED VITAL SIGN MONITOR (U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 8) PATCH SENSOR FOR MEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No. 10/906,315; filed Feb. 14, 2005); 9) PATCH SENSOR FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/160,957; filed Jul. 18, 2005); 10) WIRELESS, INTERNET-BASED SYSTEM FOR MEASURING VITAL SIGNS FROM A PLURALITY OF PATIENTS IN A HOSPITAL OR MEDICAL CLINIC (U.S. Ser. No. 11/162,719; filed Sep. 9, 2005); 11) HAND-HELD MONITOR FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/162,742; filed Sep. 21, 2005); 12) CHEST STRAP FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/306,243; filed Dec. 20, 2005); 13) SYSTEM FOR MEASURING VITAL SIGNS USING AN OPTICAL MODULE FEATURING A GREEN LIGHT SOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3, 2006); 14) BILATERAL DEVICE, SYSTEM AND METHOD FOR MONITORING VITAL SIGNS (U.S. Ser. No. 11/420,281; filed May 25, 2006); 15) SYSTEM FOR MEASURING VITAL SIGNS USING BILATERAL PULSE TRANSIT TIME (U.S. Ser. No. 11/420,652; filed May 26, 2006); 16) BLOOD PRESSURE MONITOR (U.S. Ser. No. 11/530,076; filed Sep. 8, 2006); 17) TWO-PART PATCH SENSOR FOR MONITORING VITAL SIGNS (U.S. Ser. No. 11/558,538; filed Nov. 10, 2006); and, 18) MONITOR FOR MEASURING VITAL SIGNS AND RENDERING VIDEO IMAGES (U.S. Ser. No. 11/682,177; filed Mar. 5, 2007).
Other embodiments are also within the scope of the invention. For example, other measurement techniques, such as conventional oscillometry measured during deflation, can be used to determine SYS for the above-described algorithms. Additionally, processing units and probes for measuring SpO2 similar to those described above can be modified and worn on other portions of the patient's body. For example, optical sensors with finger-ring configurations can be worn on fingers other than the thumb. Or they can be modified to attach to other conventional sites for measuring SpO2, such as the ear, forehead, and bridge of the nose. In these embodiments the processing unit can be worn in places other than the wrist, such as around the neck (and supported, e.g., by a lanyard) or on the patient's waist (supported, e.g., by a clip that attaches to the patient's belt). In still other embodiments the probe and processing unit are integrated into a single unit.
In other embodiments, a set of body-worn monitors can continuously monitor a group of patients, wherein each patient in the group wears a body-worn monitor similar to those described herein. Additionally, each body-worn monitor can be augmented with a location sensor. The location sensor includes a wireless component and a location-processing component that receives a signal from the wireless component and processes it to determine a physical location of the patient. A processing component (similar to that described above) determines from the time-dependent waveforms at least one vital sign, one motion parameter, and an alarm parameter calculated from the combination of this information. A wireless transceiver transmits the vital sign, motion parameter, location of the patient, and alarm parameter through a wireless system. A remote computer system featuring a display and an interface to the wireless system receives the information and displays it on a user interface for each patient in the group.
In embodiments, the interface rendered on the display at the central nursing station features a field that displays a map corresponding to an area with multiple sections. Each section corresponds to the location of the patient and includes, e.g., the patient's vital signs, motion parameter, and alarm parameter. For example, the field can display a map corresponding to an area of a hospital (e.g. a hospital bay or emergency room), with each section corresponding to a specific bed, chair, or general location in the area. Typically the display renders graphical icons corresponding to the motion and alarm parameters for each patient in the group. In other embodiments, the body-worn monitor includes a graphical display that renders these parameters directly on the patient.
Typically the location sensor and the wireless transceiver operate on a common wireless system, e.g. a wireless system based on 802.11 (i.e. ‘WiFi’), 802.15.4 (i.e. ‘Bluetooth’), or cellular (e.g. CDMA, GSM) protocols. In this case, a location is determined by processing the wireless signal with one or more algorithms known in the art. These include, for example, triangulating signals received from at least three different base stations, or simply estimating a location based on signal strength and proximity to a particular base station. In still other embodiments the location sensor includes a conventional global positioning system (GPS) that processes signals from orbiting satellites to determine patient's position.
The body-worn monitor can include a first voice interface, and the remote computer can include a second voice interface that integrates with the first voice interface. The location sensor, wireless transceiver, and first and second voice interfaces can all operate on a common wireless system, such as one of the above-described systems based on 802.11 or cellular protocols. The remote computer, for example, can be a monitor that is essentially identical to the monitor worn by the patient, and can be carried or worn by a medical professional. In this case the monitor associated with the medical professional features a GUI wherein the user can select to display information (e.g. vital signs, location, and alarms) corresponding to a particular patient. This monitor can also include a voice interface so the medical professional can communicate directly with the patient.
Still other embodiments are within the scope of the following claims.
This application claims priority from a Provisional Application entitled ‘BODY-WORN SYSTEM FOR CONTINUOUS, NONINVASIVE MEASUREMENT OF CARDIAC OUTPUT, STROKE VOLUME, AND BLOOD PRESSURE’, U.S. Ser. No. 61/427,756, filed Dec. 28, 2010, which is hereby incorporated by reference in its entirety.
Work for some of the components described in this patent application was sponsored by the Department of Defense under contract W81XWH-11-2-0085.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4263918 | Swearingen et al. | Apr 1981 | A |
| 4270547 | Steffen et al. | Jun 1981 | A |
| 4305400 | Logan | Dec 1981 | A |
| 4367752 | Jimenez et al. | Jan 1983 | A |
| 4577639 | Simon et al. | Mar 1986 | A |
| 4582068 | Phillipps et al. | Apr 1986 | A |
| 4653498 | New, Jr. et al. | Mar 1987 | A |
| 4710164 | Levin et al. | Dec 1987 | A |
| 4722351 | Phillipps et al. | Feb 1988 | A |
| 4802486 | Goodman et al. | Feb 1989 | A |
| 4807638 | Sramek | Feb 1989 | A |
| 4905697 | Heggs et al. | Mar 1990 | A |
| 5025791 | Niwa | Jun 1991 | A |
| 5140990 | Jones et al. | Aug 1992 | A |
| 5190038 | Polson et al. | Mar 1993 | A |
| 5197489 | Conlan | Mar 1993 | A |
| 5247931 | Norwood | Sep 1993 | A |
| 5316008 | Suga et al. | May 1994 | A |
| 5339818 | Baker et al. | Aug 1994 | A |
| 5435315 | McPhee et al. | Jul 1995 | A |
| 5448991 | Polson et al. | Sep 1995 | A |
| 5465082 | Chaco | Nov 1995 | A |
| 5482036 | Diab et al. | Jan 1996 | A |
| 5485838 | Ukawa et al. | Jan 1996 | A |
| 5490505 | Diab et al. | Feb 1996 | A |
| 5515858 | Myllymaki | May 1996 | A |
| 5517988 | Gerhard | May 1996 | A |
| 5524637 | Erickson | Jun 1996 | A |
| 5549650 | Bornzin et al. | Aug 1996 | A |
| 5575284 | Athan et al. | Nov 1996 | A |
| 5577508 | Medero | Nov 1996 | A |
| 5588427 | Tien | Dec 1996 | A |
| 5593431 | Sheldon | Jan 1997 | A |
| 5632272 | Diab et al. | May 1997 | A |
| 5649543 | Hosaka et al. | Jul 1997 | A |
| 5680870 | Hood et al. | Oct 1997 | A |
| 5685299 | Diab et al. | Nov 1997 | A |
| 5709205 | Bukta | Jan 1998 | A |
| 5743856 | Oka et al. | Apr 1998 | A |
| 5766131 | Kondo et al. | Jun 1998 | A |
| 5769785 | Diab et al. | Jun 1998 | A |
| 5800349 | Isaacson et al. | Sep 1998 | A |
| 5820550 | Polson et al. | Oct 1998 | A |
| 5848373 | DeLorme et al. | Dec 1998 | A |
| 5853370 | Chance et al. | Dec 1998 | A |
| 5857975 | Golub | Jan 1999 | A |
| 5865755 | Golub | Feb 1999 | A |
| 5865756 | Peel, III | Feb 1999 | A |
| 5873834 | Yanagi et al. | Feb 1999 | A |
| 5876353 | Riff | Mar 1999 | A |
| 5895359 | Peel, III | Apr 1999 | A |
| 5899855 | Brown | May 1999 | A |
| 5913827 | Gorman | Jun 1999 | A |
| 5919141 | Money et al. | Jul 1999 | A |
| 5941836 | Friedman | Aug 1999 | A |
| 5964701 | Asada et al. | Oct 1999 | A |
| 5964720 | Pelz | Oct 1999 | A |
| 5971930 | Elghazzawi | Oct 1999 | A |
| 6002952 | Diab et al. | Dec 1999 | A |
| 6011985 | Athan et al. | Jan 2000 | A |
| 6018673 | Chin et al. | Jan 2000 | A |
| 6036642 | Diab et al. | Mar 2000 | A |
| 6041783 | Gruenke | Mar 2000 | A |
| 6057758 | Dempsey et al. | May 2000 | A |
| 6067462 | Diab et al. | May 2000 | A |
| 6081735 | Diab et al. | Jun 2000 | A |
| 6081742 | Amano et al. | Jun 2000 | A |
| 6094592 | Yorkey et al. | Jul 2000 | A |
| 6117077 | Del Mar et al. | Sep 2000 | A |
| 6129686 | Friedman | Oct 2000 | A |
| 6157850 | Diab et al. | Dec 2000 | A |
| 6159147 | Lichter et al. | Dec 2000 | A |
| 6160478 | Jacobsen et al. | Dec 2000 | A |
| 6168569 | McEwen et al. | Jan 2001 | B1 |
| 6176831 | Voss et al. | Jan 2001 | B1 |
| 6198394 | Jacobsen et al. | Mar 2001 | B1 |
| 6198951 | Kosuda et al. | Mar 2001 | B1 |
| 6199550 | Wiesmann et al. | Mar 2001 | B1 |
| 6206830 | Diab et al. | Mar 2001 | B1 |
| 6236872 | Diab et al. | May 2001 | B1 |
| 6251080 | Henkin et al. | Jun 2001 | B1 |
| 6261247 | Ishikawa et al. | Jul 2001 | B1 |
| 6262769 | Anderson et al. | Jul 2001 | B1 |
| 6263222 | Diab et al. | Jul 2001 | B1 |
| 6287262 | Amano et al. | Sep 2001 | B1 |
| 6322516 | Masuda et al. | Nov 2001 | B1 |
| 6334065 | Al-Ali et al. | Dec 2001 | B1 |
| 6371921 | Caro et al. | Apr 2002 | B1 |
| 6388240 | Schulz et al. | May 2002 | B2 |
| RE37852 | Aso et al. | Sep 2002 | E |
| 6443890 | Schulze et al. | Sep 2002 | B1 |
| 6480729 | Stone | Nov 2002 | B2 |
| 6491647 | Bridger et al. | Dec 2002 | B1 |
| 6503206 | Li et al. | Jan 2003 | B1 |
| 6514218 | Yamamoto | Feb 2003 | B2 |
| 6516289 | David | Feb 2003 | B2 |
| 6526310 | Carter et al. | Feb 2003 | B1 |
| 6527729 | Turcott | Mar 2003 | B1 |
| 6541756 | Schulz et al. | Apr 2003 | B2 |
| 6544173 | West et al. | Apr 2003 | B2 |
| 6544174 | West et al. | Apr 2003 | B2 |
| 6546267 | Sugiura et al. | Apr 2003 | B1 |
| 6551252 | Sackner et al. | Apr 2003 | B2 |
| 6584336 | Ali et al. | Jun 2003 | B1 |
| 6589170 | Flach et al. | Jul 2003 | B1 |
| 6595929 | Stivoric et al. | Jul 2003 | B2 |
| 6605038 | Teller et al. | Aug 2003 | B1 |
| 6606993 | Wiesmann et al. | Aug 2003 | B1 |
| 6616606 | Petersen et al. | Sep 2003 | B1 |
| 6645154 | Oka | Nov 2003 | B2 |
| 6650917 | Diab et al. | Nov 2003 | B2 |
| 6684090 | Ali et al. | Jan 2004 | B2 |
| 6694177 | Eggers et al. | Feb 2004 | B2 |
| 6699194 | Diab et al. | Mar 2004 | B1 |
| 6732064 | Kadtke et al. | May 2004 | B1 |
| 6745060 | Diab et al. | Jun 2004 | B2 |
| 6770028 | Ali et al. | Aug 2004 | B1 |
| 6790178 | Mault et al. | Sep 2004 | B1 |
| 6811538 | Westbrook et al. | Nov 2004 | B2 |
| 6845256 | Chin et al. | Jan 2005 | B2 |
| 6850787 | Weber et al. | Feb 2005 | B2 |
| 6879850 | Kimball | Apr 2005 | B2 |
| 6893396 | Schulze et al. | May 2005 | B2 |
| 6912414 | Tong | Jun 2005 | B2 |
| 6934571 | Wiesmann et al. | Aug 2005 | B2 |
| 6947781 | Asada et al. | Sep 2005 | B2 |
| 6976958 | Quy | Dec 2005 | B2 |
| 6985078 | Suzuki et al. | Jan 2006 | B2 |
| 6997882 | Parker et al. | Feb 2006 | B1 |
| 7020508 | Stivoric et al. | Mar 2006 | B2 |
| 7020578 | Sorensen et al. | Mar 2006 | B2 |
| 7029447 | Rantala | Apr 2006 | B2 |
| 7041060 | Flaherty et al. | May 2006 | B2 |
| 7048687 | Reuss et al. | May 2006 | B1 |
| 7115824 | Lo | Oct 2006 | B2 |
| 7156809 | Quy | Jan 2007 | B2 |
| 7184809 | Sterling et al. | Feb 2007 | B1 |
| 7186966 | Al-Ali | Mar 2007 | B2 |
| 7194293 | Baker, Jr. | Mar 2007 | B2 |
| 7215984 | Diab et al. | May 2007 | B2 |
| 7215987 | Sterling et al. | May 2007 | B1 |
| 7225007 | Al-Ali et al. | May 2007 | B2 |
| 7237446 | Chan et al. | Jul 2007 | B2 |
| 7241265 | Cummings et al. | Jul 2007 | B2 |
| 7257438 | Kinast | Aug 2007 | B2 |
| 7296312 | Menkedick et al. | Nov 2007 | B2 |
| 7299159 | Nanikashvili | Nov 2007 | B2 |
| 7301451 | Hastings | Nov 2007 | B2 |
| 7314451 | Halperin et al. | Jan 2008 | B2 |
| 7351206 | Suzuki et al. | Apr 2008 | B2 |
| 7355512 | Al-Ali | Apr 2008 | B1 |
| 7373191 | DeLonzer et al. | May 2008 | B2 |
| 7373912 | Self et al. | May 2008 | B2 |
| 7377794 | Al-Ali et al. | May 2008 | B2 |
| 7382247 | Welch et al. | Jun 2008 | B2 |
| 7383069 | Ruchti et al. | Jun 2008 | B2 |
| 7383070 | Diab et al. | Jun 2008 | B2 |
| 7384398 | Gagnadre et al. | Jun 2008 | B2 |
| 7400919 | Petersen et al. | Jul 2008 | B2 |
| 7420472 | Tran | Sep 2008 | B2 |
| 7427926 | Sinclair et al. | Sep 2008 | B2 |
| 7455643 | Li et al. | Nov 2008 | B1 |
| 7468036 | Rulkov et al. | Dec 2008 | B1 |
| 7477143 | Albert | Jan 2009 | B2 |
| 7479890 | Lehrman et al. | Jan 2009 | B2 |
| 7485095 | Shusterman | Feb 2009 | B2 |
| 7502643 | Farringdon et al. | Mar 2009 | B2 |
| 7508307 | Albert | Mar 2009 | B2 |
| 7509131 | Krumm et al. | Mar 2009 | B2 |
| 7509154 | Diab et al. | Mar 2009 | B2 |
| 7522035 | Albert | Apr 2009 | B2 |
| 7530949 | Al Ali et al. | May 2009 | B2 |
| 7539532 | Tran | May 2009 | B2 |
| 7541939 | Zadesky et al. | Jun 2009 | B2 |
| 7542878 | Nanikashvili | Jun 2009 | B2 |
| 7586418 | Cuddihy et al. | Sep 2009 | B2 |
| 7598878 | Goldreich | Oct 2009 | B2 |
| 7602301 | Stirling et al. | Oct 2009 | B1 |
| 7616110 | Crump et al. | Nov 2009 | B2 |
| 7625344 | Brady et al. | Dec 2009 | B1 |
| 7628071 | Sasaki et al. | Dec 2009 | B2 |
| 7628730 | Watterson et al. | Dec 2009 | B1 |
| 7641614 | Asada et al. | Jan 2010 | B2 |
| 7648463 | Elhag et al. | Jan 2010 | B1 |
| 7656287 | Albert et al. | Feb 2010 | B2 |
| 7668588 | Kovacs | Feb 2010 | B2 |
| 7670295 | Sackner et al. | Mar 2010 | B2 |
| 7674230 | Reisfeld | Mar 2010 | B2 |
| 7674231 | McCombie et al. | Mar 2010 | B2 |
| 7678061 | Lee et al. | Mar 2010 | B2 |
| 7684954 | Shahabdeen et al. | Mar 2010 | B2 |
| 7689437 | Teller et al. | Mar 2010 | B1 |
| 7698101 | Alten et al. | Apr 2010 | B2 |
| 7698830 | Townsend et al. | Apr 2010 | B2 |
| 7698941 | Sasaki et al. | Apr 2010 | B2 |
| 7715984 | Ramakrishnan et al. | May 2010 | B2 |
| 7725147 | Li et al. | May 2010 | B2 |
| 7782189 | Spoonhower et al. | Aug 2010 | B2 |
| 7827011 | DeVaul et al. | Nov 2010 | B2 |
| 7925022 | Jung et al. | Apr 2011 | B2 |
| 7976480 | Grajales et al. | Jul 2011 | B2 |
| 7983933 | Karkanias et al. | Jul 2011 | B2 |
| 8047998 | Kolluri et al. | Nov 2011 | B2 |
| 8082160 | Collins, Jr. et al. | Dec 2011 | B2 |
| 8137270 | Keenan et al. | Mar 2012 | B2 |
| 8167800 | Ouchi et al. | May 2012 | B2 |
| 20010004234 | Petelenz et al. | Jun 2001 | A1 |
| 20010007923 | Yamamoto | Jul 2001 | A1 |
| 20010013826 | Ahmed et al. | Aug 2001 | A1 |
| 20020013517 | West et al. | Jan 2002 | A1 |
| 20020032386 | Sackner et al. | Mar 2002 | A1 |
| 20020072859 | Kajimoto et al. | Jun 2002 | A1 |
| 20020151805 | Sugo et al. | Oct 2002 | A1 |
| 20020156354 | Larson | Oct 2002 | A1 |
| 20020170193 | Townsend et al. | Nov 2002 | A1 |
| 20020183627 | Nishii et al. | Dec 2002 | A1 |
| 20020193671 | Ciurczak et al. | Dec 2002 | A1 |
| 20020193692 | Inukai et al. | Dec 2002 | A1 |
| 20020198679 | Victor et al. | Dec 2002 | A1 |
| 20030004420 | Narimatsu | Jan 2003 | A1 |
| 20030130590 | Bui et al. | Jul 2003 | A1 |
| 20030135099 | Al-Ali | Jul 2003 | A1 |
| 20030153836 | Gagnadre et al. | Aug 2003 | A1 |
| 20030158699 | Townsend et al. | Aug 2003 | A1 |
| 20030167012 | Friedman et al. | Sep 2003 | A1 |
| 20030171662 | O'Conner et al. | Sep 2003 | A1 |
| 20030181815 | Ebner et al. | Sep 2003 | A1 |
| 20030208335 | Unuma et al. | Nov 2003 | A1 |
| 20040019288 | Kinast | Jan 2004 | A1 |
| 20040030261 | Rantala | Feb 2004 | A1 |
| 20040034293 | Kimball | Feb 2004 | A1 |
| 20040034294 | Kimball et al. | Feb 2004 | A1 |
| 20040054821 | Warren et al. | Mar 2004 | A1 |
| 20040073128 | Hatlestad et al. | Apr 2004 | A1 |
| 20040077934 | Massad | Apr 2004 | A1 |
| 20040122315 | Krill | Jun 2004 | A1 |
| 20040133079 | Mazar et al. | Jul 2004 | A1 |
| 20040162466 | Quy | Aug 2004 | A1 |
| 20040162493 | Mills | Aug 2004 | A1 |
| 20040225207 | Bae et al. | Nov 2004 | A1 |
| 20040267099 | McMahon et al. | Dec 2004 | A1 |
| 20050027205 | Tarassenko et al. | Feb 2005 | A1 |
| 20050043598 | Goode, Jr. et al. | Feb 2005 | A1 |
| 20050059870 | Aceti | Mar 2005 | A1 |
| 20050070773 | Chin et al. | Mar 2005 | A1 |
| 20050113107 | Meunier | May 2005 | A1 |
| 20050113703 | Farringdon et al. | May 2005 | A1 |
| 20050119586 | Coyle et al. | Jun 2005 | A1 |
| 20050119833 | Nanikashvili | Jun 2005 | A1 |
| 20050124866 | Elaz et al. | Jun 2005 | A1 |
| 20050124903 | Roteliuk et al. | Jun 2005 | A1 |
| 20050149350 | Kerr et al. | Jul 2005 | A1 |
| 20050171444 | Ono et al. | Aug 2005 | A1 |
| 20050187796 | Rosenfeld et al. | Aug 2005 | A1 |
| 20050206518 | Welch et al. | Sep 2005 | A1 |
| 20050209511 | Heruth et al. | Sep 2005 | A1 |
| 20050228296 | Banet | Oct 2005 | A1 |
| 20050228298 | Banet et al. | Oct 2005 | A1 |
| 20050228301 | Banet et al. | Oct 2005 | A1 |
| 20050234317 | Kiani | Oct 2005 | A1 |
| 20050240087 | Keenan et al. | Oct 2005 | A1 |
| 20050261565 | Lane et al. | Nov 2005 | A1 |
| 20050261593 | Zhang et al. | Nov 2005 | A1 |
| 20050265267 | Hwang | Dec 2005 | A1 |
| 20050283088 | Bernstein | Dec 2005 | A1 |
| 20060036141 | Kamath et al. | Feb 2006 | A1 |
| 20060047215 | Newman et al. | Mar 2006 | A1 |
| 20060074321 | Kouchi et al. | Apr 2006 | A1 |
| 20060074322 | Nitzan | Apr 2006 | A1 |
| 20060128263 | Baird | Jun 2006 | A1 |
| 20060142648 | Banet et al. | Jun 2006 | A1 |
| 20060155589 | Lane et al. | Jul 2006 | A1 |
| 20060178591 | Hempfling | Aug 2006 | A1 |
| 20060200029 | Evans et al. | Sep 2006 | A1 |
| 20060252999 | Devaul et al. | Nov 2006 | A1 |
| 20060265246 | Hoag | Nov 2006 | A1 |
| 20060270949 | Mathie et al. | Nov 2006 | A1 |
| 20060271404 | Brown | Nov 2006 | A1 |
| 20060281979 | Kim et al. | Dec 2006 | A1 |
| 20070010719 | Huster et al. | Jan 2007 | A1 |
| 20070055163 | Asada et al. | Mar 2007 | A1 |
| 20070066910 | Inukai et al. | Mar 2007 | A1 |
| 20070071643 | Hall et al. | Mar 2007 | A1 |
| 20070094045 | Cobbs et al. | Apr 2007 | A1 |
| 20070118056 | Wang et al. | May 2007 | A1 |
| 20070129769 | Bourget et al. | Jun 2007 | A1 |
| 20070142715 | Banet et al. | Jun 2007 | A1 |
| 20070156456 | McGillin et al. | Jul 2007 | A1 |
| 20070161912 | Zhang et al. | Jul 2007 | A1 |
| 20070185393 | Zhou et al. | Aug 2007 | A1 |
| 20070188323 | Sinclair et al. | Aug 2007 | A1 |
| 20070193834 | Pai et al. | Aug 2007 | A1 |
| 20070208233 | Kovacs | Sep 2007 | A1 |
| 20070232867 | Hansmann | Oct 2007 | A1 |
| 20070237719 | Jones et al. | Oct 2007 | A1 |
| 20070244376 | Wang | Oct 2007 | A1 |
| 20070250261 | Soehren | Oct 2007 | A1 |
| 20070252853 | Park et al. | Nov 2007 | A1 |
| 20070255116 | Mehta et al. | Nov 2007 | A1 |
| 20070260487 | Bartfeld et al. | Nov 2007 | A1 |
| 20070265533 | Tran | Nov 2007 | A1 |
| 20070265880 | Bartfeld et al. | Nov 2007 | A1 |
| 20070270671 | Gal | Nov 2007 | A1 |
| 20070276261 | Banet et al. | Nov 2007 | A1 |
| 20070282208 | Jacobs et al. | Dec 2007 | A1 |
| 20070287386 | Agrawal et al. | Dec 2007 | A1 |
| 20070293770 | Bour et al. | Dec 2007 | A1 |
| 20070293781 | Sims et al. | Dec 2007 | A1 |
| 20080004500 | Cazares et al. | Jan 2008 | A1 |
| 20080004507 | Williams, Jr. et al. | Jan 2008 | A1 |
| 20080004904 | Tran | Jan 2008 | A1 |
| 20080027341 | Sackner et al. | Jan 2008 | A1 |
| 20080033255 | Essenpreis et al. | Feb 2008 | A1 |
| 20080039731 | McCombie et al. | Feb 2008 | A1 |
| 20080077027 | Allgeyer | Mar 2008 | A1 |
| 20080082001 | Hatlestad et al. | Apr 2008 | A1 |
| 20080101160 | Besson | May 2008 | A1 |
| 20080103405 | Banet et al. | May 2008 | A1 |
| 20080114220 | Banet et al. | May 2008 | A1 |
| 20080132106 | Burnes et al. | Jun 2008 | A1 |
| 20080139955 | Hansmann et al. | Jun 2008 | A1 |
| 20080146887 | Rao et al. | Jun 2008 | A1 |
| 20080146892 | LeBoeuf et al. | Jun 2008 | A1 |
| 20080161707 | Farringdon et al. | Jul 2008 | A1 |
| 20080162496 | Postrel | Jul 2008 | A1 |
| 20080167535 | Stivoric et al. | Jul 2008 | A1 |
| 20080171927 | Yang et al. | Jul 2008 | A1 |
| 20080194918 | Kulik et al. | Aug 2008 | A1 |
| 20080195735 | Hodges et al. | Aug 2008 | A1 |
| 20080204254 | Kazuno | Aug 2008 | A1 |
| 20080208013 | Zhang et al. | Aug 2008 | A1 |
| 20080208273 | Owen et al. | Aug 2008 | A1 |
| 20080214963 | Guillemaud et al. | Sep 2008 | A1 |
| 20080221399 | Zhou et al. | Sep 2008 | A1 |
| 20080221404 | Tso | Sep 2008 | A1 |
| 20080262362 | Kolluri et al. | Oct 2008 | A1 |
| 20080275349 | Halperin et al. | Nov 2008 | A1 |
| 20080281168 | Gibson et al. | Nov 2008 | A1 |
| 20080281310 | Dunning et al. | Nov 2008 | A1 |
| 20080287751 | Stivoric et al. | Nov 2008 | A1 |
| 20080294019 | Tran | Nov 2008 | A1 |
| 20080319282 | Tran | Dec 2008 | A1 |
| 20080319327 | Banet et al. | Dec 2008 | A1 |
| 20090018408 | Ouchi et al. | Jan 2009 | A1 |
| 20090018409 | Banet et al. | Jan 2009 | A1 |
| 20090018453 | Banet et al. | Jan 2009 | A1 |
| 20090040041 | Janetis et al. | Feb 2009 | A1 |
| 20090054752 | Jonnalagadda et al. | Feb 2009 | A1 |
| 20090069642 | Gao et al. | Mar 2009 | A1 |
| 20090076363 | Bly et al. | Mar 2009 | A1 |
| 20090076397 | Libbus et al. | Mar 2009 | A1 |
| 20090076398 | Li et al. | Mar 2009 | A1 |
| 20090076405 | Amurthur et al. | Mar 2009 | A1 |
| 20090082681 | Yokoyama et al. | Mar 2009 | A1 |
| 20090099424 | O'Brien et al. | Apr 2009 | A1 |
| 20090112072 | Banet et al. | Apr 2009 | A1 |
| 20090112281 | Miyazawa et al. | Apr 2009 | A1 |
| 20090112630 | Collins, Jr. et al. | Apr 2009 | A1 |
| 20090118590 | Teller et al. | May 2009 | A1 |
| 20090118626 | Moon et al. | May 2009 | A1 |
| 20090131759 | Sims et al. | May 2009 | A1 |
| 20090187085 | Pav | Jul 2009 | A1 |
| 20090192366 | Mensinger et al. | Jul 2009 | A1 |
| 20090198139 | Lewicke et al. | Aug 2009 | A1 |
| 20090221937 | Smith et al. | Sep 2009 | A1 |
| 20090222119 | Plahey et al. | Sep 2009 | A1 |
| 20090227877 | Tran | Sep 2009 | A1 |
| 20090233770 | Vincent et al. | Sep 2009 | A1 |
| 20090259113 | Liu et al. | Oct 2009 | A1 |
| 20090262074 | Nasiri et al. | Oct 2009 | A1 |
| 20090264712 | Baldus et al. | Oct 2009 | A1 |
| 20090287067 | Dorogusker et al. | Nov 2009 | A1 |
| 20090295541 | Roof | Dec 2009 | A1 |
| 20090306485 | Bell | Dec 2009 | A1 |
| 20090306487 | Crowe et al. | Dec 2009 | A1 |
| 20090306524 | Muhlsteff et al. | Dec 2009 | A1 |
| 20090312973 | Hatlestad et al. | Dec 2009 | A1 |
| 20090318779 | Tran | Dec 2009 | A1 |
| 20090322513 | Hwang et al. | Dec 2009 | A1 |
| 20090326349 | McGonigle et al. | Dec 2009 | A1 |
| 20100010380 | Panken et al. | Jan 2010 | A1 |
| 20100030034 | Schulhauser et al. | Feb 2010 | A1 |
| 20100030085 | Rojas Ojeda et al. | Feb 2010 | A1 |
| 20100056881 | Libbus et al. | Mar 2010 | A1 |
| 20100056886 | Hurtubise et al. | Mar 2010 | A1 |
| 20100113948 | Yang et al. | May 2010 | A1 |
| 20100125188 | Schilling et al. | May 2010 | A1 |
| 20100130811 | Leuthardt et al. | May 2010 | A1 |
| 20100160793 | Lee et al. | Jun 2010 | A1 |
| 20100160794 | Banet et al. | Jun 2010 | A1 |
| 20100160795 | Banet et al. | Jun 2010 | A1 |
| 20100160796 | Banet et al. | Jun 2010 | A1 |
| 20100160797 | Banet et al. | Jun 2010 | A1 |
| 20100160798 | Banet et al. | Jun 2010 | A1 |
| 20100168589 | Banet et al. | Jul 2010 | A1 |
| 20100210930 | Saylor | Aug 2010 | A1 |
| 20100217099 | LeBoeuf et al. | Aug 2010 | A1 |
| 20100222649 | Schoenberg | Sep 2010 | A1 |
| 20100234693 | Srinivasan et al. | Sep 2010 | A1 |
| 20100234786 | Fulkerson et al. | Sep 2010 | A1 |
| 20100241011 | McCombie et al. | Sep 2010 | A1 |
| 20100268518 | Sugo | Oct 2010 | A1 |
| 20100280440 | Skelton et al. | Nov 2010 | A1 |
| 20100298650 | Moon et al. | Nov 2010 | A1 |
| 20100298651 | Moon et al. | Nov 2010 | A1 |
| 20100298652 | McCombie et al. | Nov 2010 | A1 |
| 20100298653 | McCombie et al. | Nov 2010 | A1 |
| 20100298654 | McCombie et al. | Nov 2010 | A1 |
| 20100298655 | McCombie et al. | Nov 2010 | A1 |
| 20100298656 | McCombie et al. | Nov 2010 | A1 |
| 20100298657 | McCombie et al. | Nov 2010 | A1 |
| 20100298658 | McCombie et al. | Nov 2010 | A1 |
| 20100298659 | McCombie et al. | Nov 2010 | A1 |
| 20100298660 | McCombie et al. | Nov 2010 | A1 |
| 20100298661 | McCombie et al. | Nov 2010 | A1 |
| 20100312115 | Dentinger | Dec 2010 | A1 |
| 20100324384 | Moon et al. | Dec 2010 | A1 |
| 20100324385 | Moon et al. | Dec 2010 | A1 |
| 20100324386 | Moon et al. | Dec 2010 | A1 |
| 20100324387 | Moon et al. | Dec 2010 | A1 |
| 20100324388 | Moon et al. | Dec 2010 | A1 |
| 20100324389 | Moon et al. | Dec 2010 | A1 |
| 20100331640 | Medina | Dec 2010 | A1 |
| 20110066006 | Banet et al. | Mar 2011 | A1 |
| 20110066007 | Banet et al. | Mar 2011 | A1 |
| 20110066008 | Banet et al. | Mar 2011 | A1 |
| 20110066009 | Moon et al. | Mar 2011 | A1 |
| 20110066010 | Moon et al. | Mar 2011 | A1 |
| 20110066037 | Banet et al. | Mar 2011 | A1 |
| 20110066038 | Banet et al. | Mar 2011 | A1 |
| 20110066039 | Banet et al. | Mar 2011 | A1 |
| 20110066043 | Banet et al. | Mar 2011 | A1 |
| 20110066044 | Moon et al. | Mar 2011 | A1 |
| 20110066045 | Moon et al. | Mar 2011 | A1 |
| 20110066050 | Moon et al. | Mar 2011 | A1 |
| 20110066051 | Moon et al. | Mar 2011 | A1 |
| 20110066062 | Banet et al. | Mar 2011 | A1 |
| 20110070829 | Griffin et al. | Mar 2011 | A1 |
| 20110076942 | Taveau et al. | Mar 2011 | A1 |
| 20110093281 | Plummer et al. | Apr 2011 | A1 |
| 20110105862 | Gies et al. | May 2011 | A1 |
| 20110125207 | Nabutovsky et al. | May 2011 | A1 |
| 20110144456 | Muhlsteff et al. | Jun 2011 | A1 |
| 20110152632 | Le Neel et al. | Jun 2011 | A1 |
| 20110178375 | Forster | Jul 2011 | A1 |
| 20110224498 | Banet et al. | Sep 2011 | A1 |
| 20110224499 | Banet et al. | Sep 2011 | A1 |
| 20110224500 | Banet et al. | Sep 2011 | A1 |
| 20110224506 | Moon et al. | Sep 2011 | A1 |
| 20110224507 | Banet et al. | Sep 2011 | A1 |
| 20110224508 | Moon | Sep 2011 | A1 |
| 20110224556 | Moon et al. | Sep 2011 | A1 |
| 20110224557 | Banet et al. | Sep 2011 | A1 |
| 20110224564 | Moon et al. | Sep 2011 | A1 |
| 20110257489 | Banet et al. | Oct 2011 | A1 |
| 20110257551 | Banet et al. | Oct 2011 | A1 |
| 20110257552 | Banet et al. | Oct 2011 | A1 |
| 20110257554 | Banet et al. | Oct 2011 | A1 |
| 20110257555 | Banet et al. | Oct 2011 | A1 |
| 20110275907 | Inciardi et al. | Nov 2011 | A1 |
| 20120065525 | Douniama et al. | Mar 2012 | A1 |
| 20120123232 | Najarian et al. | May 2012 | A1 |
| Number | Date | Country |
|---|---|---|
| 0443267 | Aug 1991 | EP |
| 0993803 | Apr 2000 | EP |
| 2329250 | Mar 1999 | GB |
| 9932030 | Jul 1999 | WO |
| 2006005169 | Jan 2006 | WO |
| 2007024777 | Mar 2007 | WO |
| 2007143535 | Dec 2007 | WO |
| 2008037820 | Apr 2008 | WO |
| 2008110788 | Sep 2008 | WO |
| 2009009761 | Jan 2009 | WO |
| 2010135516 | Nov 2010 | WO |
| 2010135518 | Nov 2010 | WO |
| 2010148205 | Dec 2010 | WO |
| 2011032132 | Mar 2011 | WO |
| 2011034881 | Mar 2011 | WO |
| 2011082341 | Jul 2011 | WO |
| 2011112782 | Sep 2011 | WO |
| 2011133582 | Oct 2011 | WO |
| Entry |
|---|
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 30, 2012 in U.S. Appl. No. 12/469,202. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Sep. 17, 2012 in U.S. Appl. No. 12/650,354. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Sep. 21, 2012 in U.S. Appl. No. 12/469,115. |
| Response to Non-Final Office Action issued in U.S. Appl. No. 12/469,236 dated Sep. 27, 2012. |
| Response to Non-Final Office Action issued in U.S. Appl. No. 12/487,283 dated Sep. 27, 2012. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Sep. 28, 2012 in U.S. Appl. No. 12/560,087. |
| Response to Non-Final Office Action issued in U.S. Appl. No. 12/762,836 dated Oct. 9, 2012. |
| Response to Non-Final Office Action issued in U.S. Appl. No. 12/559,429 dated Oct. 12, 2012. |
| Response to Non-Final Office Action issued in U.S. Appl. No. 12/559,430 dated Oct. 12, 2012. |
| Response to Non-Final Office Action issued in U.S. Appl. No. 12/559,435 dated Oct. 23, 2012. |
| Final Office Action issued by the US Patent and Trademark Office on Oct. 24, 2012 in U.S. Appl. No. 12/599,429. |
| Final Office Action issued by the US Patent and Trademark Office on Oct. 24, 2012 in U.S. Appl. No. 12/599,430. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Oct. 23, 2012 in U.S. Appl. No. 12/762,944. |
| Response to Non-Final Office Action issued in U.S. Appl. No. 12/762,733 dated Oct. 25, 2012. |
| Final Office Action issued by the US Patent and Trademark Office on Oct. 26, 2012 in U.S. Appl. No. 12/762,836. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Oct. 24, 2012 in U.S. Appl. No. 12/559,403. |
| Non Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/559,413 on Nov. 9, 2012. |
| Response to Office Action issued in U.S. Appl. No. 12/762,846 dated Nov. 13, 2012. |
| Response to Office Action issued in U.S. Appl. No. 12/762,874 dated Nov. 13, 2012. |
| Response to Office Action issued in U.S. Appl. No. 12/560,111 dated Nov. 26, 2012. |
| Response to Office Action issued in U.S. Appl. No. 11/930,881 dated Nov. 26, 2012. |
| Final Rejection issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/559,419 on Nov. 16, 2012. |
| Non Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/559,408 on Nov. 23, 2012. |
| Response to Office Action issued in U.S. Appl. No. 12/138,199 dated Nov. 29, 2012. |
| Response to Office Action issued in U.S. Appl. No. 12/650,383 dated Dec. 7, 2012. |
| Response to Office Action issued in U.S. Appl. No. 12/650,392 dated Dec. 7, 2012. |
| Final Rejection issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/559,435 on Dec. 12, 2012. |
| Final Rejection issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/560,111 on Dec. 12, 2012. |
| Clifford et al., Measuring Tilt with Low-g Accelerometers. Freescale Semiconductor, Inc., 2005:8 pages. |
| McKneely et al., Plug-and-Play and Network-Capable Medical Instrumentation and Database with a Complete Healthcare Technology Suite: MediCAN. Joint Workshop on High Confidence Medical Devices, Software, and Systems and Medical Device Plug-and-Play Interoperability. 2007:122-129. |
| Montgomery et al., Lifeguard—A Personal Physiological Monitor for Extreme Environments. Conf Proc IEEE Eng Med Biol Soc. 2004;3:2192-2195. |
| Thongpithoonrat et al., Networking and Plug-and-Play of Bedside Medical Instruments. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:1514-1517. |
| Yang et al., Research on Multi-Parameter Physiological Monitor Based on CAN Bus. IFMBE Proceed. 2008;19:417-419. |
| Zeltwanger, Controller Area Network and CANopen in Medical Equipment. Bus Briefing: Med Dev Manuf Technol. 2002:34-37. |
| Zitzmann and Schumann, Interoperable Medical Devices Due to Standardized CANopen Interfaces. Joint Workshop on High Confidence Medical Devices, Software, and Systems and Medical Device Plug-and-Play Interoperability. 2007:97-103. |
| Non Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 13/432,976 on Dec. 14, 2012. |
| Final Rejection issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/762,733 on Dec. 20, 2012. |
| Final Rejection issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/762,846 on Dec. 20, 2012. |
| Final Rejection issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/650,392 on Jan. 3, 2013. |
| Final Rejection issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/487,283 on Jan. 3, 2013. |
| Non Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 13/292,923 on Jan. 14, 2013. |
| Notice of Allowance issued by the United States Patent and Trademark Office in U.S. Appl. No. 11/470,708 on Jan. 18, 2013. |
| International Search Report and Written Opinion issued in PCT/US2012/064302 on Jan. 15, 2013. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Jul. 18, 2012 in U.S. Appl. No. 12/650,389. |
| Chan et al., Noninvasive and Cuffless Measurements of Blood Pressure for Telemedicine. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2001:3 pages. |
| Fung, Advisory System for Administration of Phenylephrine Following Spinal Anesthesia for Cesarean Section. Master's Thesis. University of British Columbia 2002: 119 pages. |
| Liu et al., The Changes in Pulse Transit Time at Specific Cuff Pressures during Inflation and Deflation. Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug. 30-Sep. 3, 2006:6404-6405. |
| Nitzan et al., Effects of External Pressure on Arteries Distal to the Cuff During Sphygmomanometry. IEEE Transactions on Biomedical Engineering, Jun. 2005;52(6):1120-1127. |
| USB 2.0 Specification Engineering Change Notice. Oct. 20, 2000. |
| Yan and Zhang, A Novel Calibration Method for Noninvasive Blood Pressure Measurement Using Pulse Transit Time. Proceedings of the 4th IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors St Catharine's College,Cambridge, UK, Aug. 19-22, 2007. |
| Zislin et al., Ways of Improving the Accuracy of Arterial Pressure Oscillometry. Biomedical Engineering 2005;39 (4):174-178. |
| International Search Report and Written Opinion dated May 29, 2012 issued in PCT/US2012/025648. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 3, 2012 in U.S. Appl. No. 12/762,925. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 3, 2012 in U.S. Appl. No. 12/762,963. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 20, 2012 in U.S. Appl. No. 12/762,777. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 21, 2012 in U.S. Appl. No. 12/469,107. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 24, 2012 in U.S. Appl. No. 12/762,936. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Jun. 11, 2012 in U.S. Appl. No. 12/469,222. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Jun. 8, 2012 in U.S. Appl. No. 12/650,383. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Jun. 8, 2012 in U.S. Appl. No. 12/650,392. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Jun. 20, 2012 in U.S. Appl. No. 12/762,751. |
| International Search Report and Written Opinion dated Jun. 29, 2012 issued in PCT/US2012/025640. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Jul. 5, 2012 in U.S. Appl. No. 12/560,138. |
| “Signal Strength.” Oct. 6, 2008. http://web.archive.org/web/20081 06200523/http:/!en.wikipedia.org/wiki/Signal—strength. |
| Non-Final Office Action issued by the US Patent and Trademark Office on May 24, 2012 in U.S. Appl. No. 12/560,111. |
| Restriction Requirement issued by the US Patent and Trademark Office on Apr. 24, 2012 in U.S. Appl. No. 12/469,107. |
| Response to Restriction Requirement submitted Jun. 14, 2012 in U.S. Appl. No. 12/469,107. |
| Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/469,236 dated Jul. 8, 2013. |
| Non-Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/560,111 dated Jul. 8, 2013. |
| Scanaill et al., A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment. Annals of Biomed Engineer. Apr. 2006;34(4):547-563. |
| Notice of Allowance issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/762,733 dated Jul. 24, 2013. |
| Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/762,944 dated Aug. 2, 2013. |
| Response to Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/559,039 dated Aug. 9, 2013. |
| Sifil et al., Evaluation of the Harmonized Alert Sensing Technology Device for Hemodynamic Monitoring in Chronic Hemodialysis Patients. ASAIO J. Nov.-Dec. 2003;49(6):667-672. |
| Weinhold et al., Buprenorphine alone and in combination with naloxone in non-dependent humans. Drug Alcohol Depend. Aug. 1992;30(3):263-274. |
| Final Office Action issued by the United States Patent and Trademark Office in U.S. Appl. No. 12/762,726 dated Aug. 15, 2013. |
| Extended European Search Report and Written Opinion issued in application No. EP 10817733 dated Aug. 29, 2013. |
| Extended European Search Report and Written Opinion issued in application No. EP 08770884 dated Sep. 17, 2013. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 31, 2012 in U.S. Appl. No. 12/469,213. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Sep. 14, 2012 in U.S. Appl. No. 12/650,374. |
| Drinnan et al., Relation between heart rate and pulse transit time during paced respiration. Physiol. Meas. Aug. 2001;22(3):425-432. |
| Flash et al., The Coordination of Arm Movements: An Experimentally Confirmed Mathematical Model. J Neurosci. Jul. 1985;5(7):1688-1703. |
| Ma and Zhang, A Correlation Study on the Variabilities in Pulse Transit Time, Blood Pressure, and Heart Rate Recorded Simultaneously from Healthy Subjects. Conf Proc IEEE Eng Med Biol Soc. 2005;1:996-999. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Sep. 17, 2012 in U.S. Appl. No. 12/469,192. |
| Gallagher, Comparison of Radial and Femoral Arterial Blood Pressure in Children after Cardiopulmonary Bypass. J Clin Monit. Jul. 1985;1(3):168-171. |
| Park et al., Direct Blood Pressure Measurements in Brachial and Femoral Arteries in Children. Circulation Feb. 1970; 41(2)231-237. |
| Talkowski, Quantifying Physical Activity in Community Dwelling Older Adults Using Accelerometry. University of Pittsburgh (Dissertation) 2008:1-91. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Sep. 26, 2012 in U.S. Appl. No. 12/560,104. |
| Packet Definition. The Linux Information Project Jan. 8, 2006 http://www.linfo.org/packet.html. |
| RS-232. Wikipedia Dec. 5, 2008 http:I/web.archive.org/web/20081205160754/http:/len.wikipedia.org/wiki/RS-232. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Oct. 9, 2012 in U.S. Appl. No. 12/762,726. |
| Final Office Action issued by the US Patent and Trademark Office on Oct. 22, 2012 in U.S. Appl. No. 12/762,822. |
| Final Office Action issued by the US Patent and Trademark Office on Oct. 25, 2012 in U.S. Appl. No. 12/599,426. |
| Alves et al., CAN Protocol: A Laboratory Prototype for Fieldbus Applications. XIX IMEKO World Congress Fundamental and Applied Metrology Sep. 6-11, 2009 Lisbon, Portugal. 4 pages :454-457 ISBN 978-963-88410-0-1. |
| Benefits of Digital Sensors. Gems Sensors. Feb. 14, 2008. http://web.archive.org/web/20080214122230/http://www.sensorland.com/HowPage054.html. |
| Final Office Action issued by the US Patent and Trademark Office on Oct. 25, 2012 in U.S. Appl. No. 12/762,790. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Oct. 30, 2012 in U.S. Appl. No. 12/559,386. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Nov. 6, 2012 in U.S. Appl. No. 12/559,379. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Nov. 6, 2012 in U.S. Appl. No. 12/650,370. |
| Poon and Zhang, Cuff-Less and Noninvasive Measurements of Arterial Blood Pressure by Pulse Transit Time. Conf Proc IEEE Eng Med Biol Soc. 2005;6:5877-5880. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Nov. 7, 2012 in U.S. Appl. No. 12/559,392. |
| International Search Report and Written Opinion dated Jul. 22, 2011 issued in PCT/US2011/027843. |
| International Search Report and Written Opinion dated Jul. 20, 2011 issued in PCT/US2011/033100. |
| Non-Final Office Action issued by the US Patent and Trademark Office on May 26, 2011 in U.S. Appl. No. 12/469,151. |
| Response to Non-Final Office Action submitted Nov. 25, 2011 in U.S. Appl. No. 12/469,151. |
| Notice of Allowance issued by the US Patent and Trademark Office on Feb. 1, 2012 in U.S. Appl. No. 12/469,151. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Aug. 4, 2011 in U.S. Appl. No. 12/469,182. |
| Response to Non-Final Office Action submitted Nov. 25, 2011 in U.S. Appl. No. 12/469,182. |
| Notice of Allowance issued by the US Patent and Trademark Office on Dec. 28, 2011 in U.S. Appl. No. 12/469,182. |
| International Search Report and Written Opinion dated Oct. 15, 2010 issued in PCT/US2010/035550. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 12, 2012 in U.S. Appl. No. 12/559,429. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 12, 2012 in U.S. Appl. No. 12/559,430. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 24, 2012 in U.S. Appl. No. 12/559,435. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 25, 2012 in U.S. Appl. No. 12/762,733. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 27, 2012 in U.S. Appl. No. 12/762,822. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Mar. 27, 2012 in U.S. Appl. No. 12/559,422. |
| Mathie, Monitoring and Interpreting Human Movement Patterns using a Triaxial Accelerometer. Faculty of Engineering. The University of New South Wales. PhD Dissertation. Aug. 2003: part1 pp. 1-256. |
| Mathie, Monitoring and Interpreting Human Movement Patterns using a Triaxial Accelerometer. Faculty of Engineering. The University of New South Wales. PhD Dissertation. Aug. 2003: part2 pp. 256-512. |
| International Search Report and Written Opinion dated Apr. 27, 2012 as reported in PCT/US2011/067441. |
| Non-Final Office Action issued by the US Patent and Trademark Office on May 7, 2012 in U.S. Appl. No. 12/469,115. |
| Non-Final Office Action issued by the US Patent and Trademark Office on May 9, 2012 in U.S. Appl. No. 12/762,836. |
| Non-Final Office Action issued by the US Patent and Trademark Office on May 10, 2012 in U.S. Appl. No. 12/559,419. |
| Jackson, Digital Filter Design and Synthesis Using High-Level Modeling Tools. Virginia Polytechnic Institute and State University Thesis. Dec. 1999. |
| Kim et al., Two Algorithms for Detecting Respiratory Rate from ECG Signal. IFMBE Proceedings 2007;14(6) JC27:4069-4071. |
| O'Haver, Peak Finding and Measurement, Version 1.6 Oct. 26, 2006. http://web.archive.org/web/20090205162604/http://terpconnect.umd.edu/-toh/spectrum/PeakFindingandMeasurement.htm. |
| Reinvuo et al., Measurement of Respiratory Rate with High-Resolution Accelerometer and EMFit Pressure Sensor. Proceedings of the 2006 IEEE Sensors Applications Symposium Feb. 7-9, 2006:192-195. |
| Non-Final Office Action issued by the US Patent and Trademark Office on May 11, 2012 in U.S. Appl. No. 12/762,846. |
| Non-Final Office Action issued by the US Patent and Trademark Office on May 11, 2012 in U.S. Appl. No. 12/762,874. |
| Allen et al., Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. Physiol. Meas. 2006;27:935-951. |
| Asada et al., Active Noise Cancellation Using MEMS Accelerometers for Motion-Tolerant Wearable Bio-Sensors. Proceedings of the 26th Annual International Conference of the IEEE Embs. San Francisco, CA, USA. Sep. 1-5, 2004:2157-2160. |
| Bowers et al., Respiratory Rate Derived from Principal Component Analysis of Single Lead Electrocardiogram. Computers in Cardiology Conference Proceedings Sep. 2008;35:437-440. |
| Bussmann et al., Measuring daily behavior using ambulatory accelerometry: The Activity Monitor. Behav Res Methods Instrum Comput. Aug. 2001;33(3):349-356. |
| Cretikos et al., The Objective Medical Emergency Team Activation Criteria: a case-control study. Resuscitation Apr. 2007;73(1):62-72. |
| Espina et al., Wireless Body Sensor Network for Continuous Cuff-less Blood Pressure Monitoring. Proceedings of the 3rd IEEE-EMBS. International Summer School and Symposium on Medical Devices and Biosensors. MIT, Boston, USA, Sep. 4-6, 2006:11-15. |
| Fieselmann et al., Respiratory rate predicts cardiopulmonary arrest for internal medicine patients. J Gen Intern Med Jul. 1993;8(7):354-360. |
| Goldhill et al., A physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia Jun. 2005;60(6):547-553. |
| Hung et al., Estimation of Respiratory Waveform Using an Accelerometer. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, May 14-17, 2008:1493-1496. |
| Jin, A Respiration Monitoring System Based on a Tri-Axial Accelerometer and an Air-Coupled Microphone. Technische Universiteit Eindhoven, University of Technology. Master's Graduation Paper, Electrical Engineering Aug. 25, 2009. |
| Karantonis et al., Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring. IEEE Transactions on Information Technology in Biomedicine. Jan. 2006;10(1):156-167. |
| Khambete et al., Movement artefact rejection in impedance pneumography using six strategically placed electrodes. Physiol. Meas. 2000;21:79-88. |
| Khan et al., Accelerometer Signal-based Human Activity Recognition Using Augmented Autoregressive Model Coefficients and Artificial w Neural Nets. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Aug. 20-24, 2008:5172-5175. |
| Mason, Signal Processing Methods for Non-Invasive Respiration Monitoring. Department of Engineering Science, University of Oxford 2002. |
| Mathie et al., Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput. Sep. 2004;42(5):679-687. |
| Otto et al., System Architecture of a Wireless Body Area Sensor Network for Ubiquitous Health Monitoring. Journal of Mobile Multimedia Jan. 10, 2006;1(4):307-326. |
| Park et al., An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation. Med Bio Eng Comput 2008;46:147-158. |
| PDF-Pro for iPhone & iPod touch User Manual. ePapyrus Jul. 2009;1:1-25 http://epapyrus.com/en/files/PDFPro%. |
| Seo et al., Performance Improvement of Pulse Oximetry-Based Respiration Detection by Selective Mode Bandpass Filtering. Ergonomics and Health Aspects of Work with Computers Lecture Notes in Computer Science, 2007;4566:300-308. |
| Soh et al., An investigation of respiration while wearing back belts. Applied Ergonomics 1997; 28(3):189-192. |
| Subbe et al., Effect of introducing the Modified Early Warning score on clinical outcomes, cardiopulmonary arrests and intensive care utilization in acute medical admissions. Anaesthesia Aug. 2003;58(8):797-802. |
| Vuorela et al., Two portable long-term measurement devices for ECG and bioimpedance. Second International Conference on Pervasive Computing Technologies for Healthcare.. Jan. 30-Feb. 1, 2008: 169-172. |
| Wolf et al., Development of a Fall Detector and Classifier based on a Triaxial Accelerometer Demo Board. 2007:210-213. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 30, 2012 in U.S. Appl. No. 12/762,790. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Mar. 30, 2012 in U.S. Appl. No. 12/469,236. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 3, 2012 in U.S. Appl. No. 12/469,094. |
| Restriction Requirement issued by the US Patent and Trademark Office on Feb. 2, 2012 in U.S. Appl. No. 12/469,222. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Mar. 27, 2012 in U.S. Appl. No. 12/559,426. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 3, 2012 in U.S. Appl. No. 12/559,039. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Dec. 29, 2011 in U.S. Appl. No. 12/559,080. |
| Response to Non-Final Office Action submitted Mar. 19, 2012 in U.S. Appl. No. 12/559,080. |
| Notice of Allowance issued by the US Patent and Trademark Office on Apr. 2, 2012 in U.S. Appl. No. 12/559,080. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Dec. 15, 2011 in U.S. Appl. No. 12/560,077. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Mar. 8, 2012 in U.S. Appl. No. 12/560,093. |
| Restriction Requirement issued by the US Patent and Trademark Office on Dec. 14, 2012 in U.S. Appl. No. 12/560,093. |
| Response to Restriction Requirement submitted Feb. 15, 2012 in U.S. Appl. No. 12/560,093. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Mar. 1, 2012 in U.S. Appl. No. 12/560,104. |
| Restriction Requirement issued by the US Patent and Trademark Office on Jan. 19, 2012 in U.S. Appl. No. 12/469,115. |
| Response to Restriction Requirement submitted Feb. 15, 2012 in U.S. Appl. No. 12/469,115. |
| Restriction Requirement issued by the US Patent and Trademark Office on Nov. 14, 2011 in U.S. Appl. No. 12/469,127. |
| Response to Restriction Requirement submitted Feb. 15, 2012 in U.S. Appl. No. 12/469,127. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Mar. 9, 2012 in U.S. Appl. No. 12/469,127. |
| Non-Final Office Action issued by the US Patent and Trademark Office on Apr. 3, 2012 in U.S. Appl. No. 12/469,137. |
| International Preliminary Report on Patentability dated Dec. 1, 2011 issued in PCT/US2010/035554. |
| International Search Report and Written Opinion dated Sep. 23, 2010 issued in PCT/US2010/035554. |
| International Preliminary Report on Patentability dated Jan. 5, 2012 issued in PCT/US2010/039000. |
| International Search Report and Written Opinion dated Sep. 7, 2010 issued in PCT/US2010/039000. |
| International Search Report and Written Opinion dated Nov. 3, 2010 issued in PCT/US2010/048729. |
| International Search Report and Written Opinion dated Nov. 5, 2010 issued in PCT/US2010/048866. |
| International Search Report and Written Opinion dated Mar. 3, 2011 issued in PCT/US2010/062564. |
| Henry et al., “Stroke Volume Obtained from the Brachial Artery Using Transbrachial Electrical Bioimpedance Velocimetry”, 34th Annual International Conference of the IEEE EMBS, San Diego, California USA, Aug. 28- Sep. 1, 2012, pp. 142-145. |
| Number | Date | Country | |
|---|---|---|---|
| 20140249442 A1 | Sep 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| 61427756 | Dec 2010 | US |
