PHYSIOLOGICAL MONITOR FOR MONITORING PATIENTS UNDERGOING HEMODIALYSIS

Abstract

The invention provides a system for characterizing a patient undergoing hemodialysis, featuring: 1) a body-worn biometric sensor, worn on a single location of the patient, and featuring: i) sensing elements for measuring electrocardiogram (ECG), thoracic bio-impedance (TBI), photoplethysmogram (PPG), and phonocardiogram (PCG) waveforms; ii) a processor for collectively analyzing the ECG, TBI, PPG, and PCG waveforms to determine a set of physiological parameters; and iii) a first wireless transceiver configured to transmit the set of physiological parameters; 2) a gateway system comprising a second wireless transceiver configured to receive the set of physiological parameters; and 3) a data-analytics system configured to analyze the set of physiological parameters to determine the patient's status.

Claims

1. A system for characterizing a patient undergoing a hemodialysis session, comprising: a body-worn biometric sensor, worn completely on a patient's body on a single location, and comprising: 1) sensing elements for measuring electrocardiogram (ECG), thoracic bio-impedance (TBI), photoplethysmogram (PPG), and phonocardiogram (PCG) waveforms; 2) a processor for collectively analyzing the ECG, TBI, PPG, and PCG waveforms to determine a set of physiological parameters; and 3) a first wireless transceiver configured to transmit the set of physiological parameters; a gateway system comprising: a second wireless transceiver configured to receive the set of physiological parameters; and a data-analytics system configured to analyze the set of physiological parameters to determine a status of the patient.

2. A system for estimating a dry weight value of a patient undergoing a hemodialysis session, comprising: a body-worn biometric sensor, worn on a single location of the patient, and comprising: 1) sensing elements for measuring thoracic bio-impedance (TBI) waveforms; 2) a processor for collectively analyzing the TBI waveforms to estimate a fluid value of the patient, and then estimating the dry weight value of the patient by analyzing the fluid value and a value of the patient's weight before the hemodialysis session begins.

3. A system for characterizing a set of patients undergoing a hemodialysis session, comprising: a set of body-worn biometric sensors, each sensor configured to be worn on a single location of a patient in the set of patients and comprising: 1) sensing elements for measuring electrocardiogram (ECG), thoracic bio-impedance (TBI), photoplethysmogram (PPG), and phonocardiogram (PCG) waveforms; 2) a processor for collectively analyzing the ECG, TBI, PPG, and PCG waveforms to determine a set of physiological parameters; and 3) a first wireless transceiver configured to transmit the set of physiological parameters; and a gateway system comprising: a second wireless transceiver configured to receive the set of physiological parameters from each body-worn biometric sensor in the set of body-worn biometric sensors, the gateway system configured to automatically wirelessly pair with and then download a first set of information from a first body-worn biometric sensor in the set, and then once finished automatically wirelessly pair with and then download a second set of information from a second body-worn biometric sensor in the set, the gateway system further configured to repeat this process until sets of information are downloaded from each body-worn biometric sensor in the set of body-worn biometric sensors.

4. A system for estimating a fluid level of a patient undergoing a hemodialysis session, comprising: a body-worn biometric sensor, worn on a region of the patient proximal to the upper thoracic cavity, and comprising: 1) sensing elements for measuring thoracic bio-impedance (TBI) waveforms for the patient's upper thoracic cavity; and 2) a processor for collectively analyzing the TBI waveforms to determine a fluid value of the patient representing fluid levels in the patient's entire thoracic cavity.

5. A system for characterizing blood pressure values from a patient undergoing a hemodialysis session, comprising: a body-worn biometric sensor, worn on a single location of the patient, and comprising: 1) sensing elements for measuring electrocardiogram (ECG), thoracic bio-impedance (TBI), photoplethysmogram (PPG), and phonocardiogram (PCG) waveforms; 2) an interface to receive a calibration blood pressure measurement from a cuff-based system; 3) a processor for collectively analyzing the ECG, TBI, PPG, and PCG waveforms and the calibration blood pressure measurement to determine a cuffless blood pressure value; and 3) a first wireless transceiver configured to transmit the cuffless blood pressure value; a gateway system comprising: 1) a second wireless transceiver configured to receive the cuffless blood pressure value; and a data-analytics system configured to analyze the cuffless blood pressure value to determine a status of the patient.

6. A sensor for measuring a blood pressure value from a patient, comprising: a set of four electrodes, with two electrodes in the set connected to an electrical circuit configured to inject electrical current into the patient, and two separate electrodes in the set connected to an electrical circuit configured to sense a voltage from the patient's chest; an analog system comprising a first analog filter configured to process the voltage to determine an impedance waveform, and a second analog filter configured to process the voltage to determine an ECG waveform; and a processor configured to process the ECG waveform to determine a first fiducial point, and process the impedance waveform to determine a second fiducial point, and then process a time difference between the first and second fiducial point to determine the blood pressure value; the sensor worn completely on the patient's body and also comprising a wireless transmitter for transmitting information to an external gateway system.

7. A sensor for measuring a stroke volume value from a patient, comprising: a set of four electrodes, with two electrodes in the set connected to an electrical circuit configured to inject electrical current into the patient, and two separate electrodes in the set connected to an electrical circuit configured to sense a voltage from the patient's chest; an analog system configured to process the voltage to determine a thoracic bio-impedance (TBI) waveform; a sensor configured to measure a phonocardiogram (PCG) waveform; and a processor configured to process the PCG waveform to determine S1 and S2 heart sounds, and from the time difference between the S1 and S2 heart sounds determine a left ventricular ejection time (LVET), the processor further configured to process the impedance waveform to determine a fiducial point, and then process LVET and the fiducial point to determine the stroke volume value; the sensor worn completely on the patient's body and also comprising a wireless transmitter for transmitting information to an external gateway system.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] FIG. 1 is a schematic drawing showing a set of patients undergoing hemodialysis, with each patient wearing a sensor that transmits information to a central station coupled to a data-analytics engine according to the invention;

[0037] FIG. 2 is a flow chart of an algorithm used with the data-analytics engine of FIG. 1 to predict decompensation occurring in a patient undergoing hemodialysis;

[0038] FIG. 3 is a photograph of a front portion of a sensor according to the invention;

[0039] FIG. 4A is a photograph of a back portion of the sensor according to the invention, with exposed electrode contact points;

[0040] FIG. 4B is a photograph of a back portion of the sensor according to the invention, with disposable patch electrodes connected to the exposed electrode contact points;

[0041] FIG. 5A is a time-dependent plot of an ECG waveform collected from a patient;

[0042] FIG. 5B is a time-dependent plot of a PCG waveform collected simultaneously and from the same patient as the ECG waveform shown in FIG. 5A;

[0043] FIG. 5C is a time-dependent plot of a PPG waveform collected simultaneously and from the same patient as the ECG waveform shown in FIG. 5A;

[0044] FIG. 5D is a time-dependent plot of a TBI waveform collected simultaneously and from the same patient as the ECG waveform shown in FIG. 5A;

[0045] FIG. 5E is a time-dependent plot of a first derivative of the TBI waveform shown in FIG. 5D;

[0046] FIGS. 5A-5E are time-dependent plots of the following waveforms collected from a patient: ECG (FIG. 5A), PCG (FIG. 5B), PPG (FIG. 5C), AC component of TBI (FIG. 5D), and derivative of the AC component of TBI (FIG. 5E);

[0047] FIG. 6A is a time-dependent plot of ECG and PPG waveforms generated with the sensor of FIG. 3 and from a single heartbeat from a patient, along with circular symbols marking fiducial points in these waveforms and indicating a time interval related to electro-mechanical activation time (EMAT);

[0048] FIG. 6B is a time-dependent plot of TBI and PPG waveforms generated with the sensor of FIG. 3 and from a single heartbeat from a patient, along with circular symbols marking fiducial points in these waveforms and indicating a time interval related to a first VTT (VTT.sub.1);

[0049] FIG. 6C is a time-dependent plot of PCG and PPG waveforms generated with the sensor of FIG. 3 and from a single heartbeat from a patient, along with circular symbols marking fiducial points in these waveforms and indicating a time interval related to a third VTT (VTT.sub.3);

[0050] FIG. 6D is a time-dependent plot of ECG and PPG waveforms generated with the sensor of FIG. 3 and from a single heartbeat from a patient, along with circular symbols marking fiducial points in these waveforms and indicating a time interval related to PAT;

[0051] FIG. 6E is a time-dependent plot of PCG and TBI waveforms generated with the sensor of FIG. 3 and from a single heartbeat from a patient, along with circular symbols marking fiducial points in these waveforms and indicating a time interval related to a second VTT (VTT.sub.2);

[0052] FIG. 6F is a time-dependent plot of a PCG waveform generated with the sensor of FIG. 3 and from a single heartbeat from a patient, along with circular symbols marking fiducial points in this waveform and indicating a time interval related to LVET;

[0053] FIG. 7A is a schematic drawing of a patient wearing the sensor, whose BP measurement is calibrated using a cuff-based system;

[0054] FIG. 7B is a schematic drawing of a patient wearing the sensor after it has been calibrated using a cuff-based system;

[0055] FIG. 8A is a photograph of a set of sensors attached to a charging station while information therefrom is wirelessly downloaded;

[0056] FIG. 8B is a photograph of a software user interface operating on a tablet computer gateway that downloads information from the set of sensors shown in FIG. 8A;

[0057] FIG. 9A is a photograph of a manikin showing where TFC values are measured according to the sensor of the invention (referred to below as a test device);

[0058] FIG. 9B is a photograph of a manikin showing where Z.sub.0 values are measured with a reference device used in a clinical trial described herein;

[0059] FIG. 10A is a scatterplot showing impedance values, measured as a function of fluid removed during hemodialysis for both the test and reference devices, where the values for the test and reference devices diverge;

[0060] FIG. 10B is a correlation plot showing agreement between measurements made by test and reference devices, as shown in FIG. 10A;

[0061] FIG. 10C is a scatterplot showing impedance values, measured as a function of fluid removed during hemodialysis for both the test and reference devices, where the values for the test and reference devices converge;

[0062] FIG. 10D is a correlation plot showing agreement between measurements made by test and reference devices, as shown in FIG. 10C;

[0063] FIG. 11A is a scatterplot showing impedance values, measured as a function of fluid removed during hemodialysis for both the test and reference devices, where the values for the test and reference devices are relatively high;

[0064] FIG. 11B is a correlation plot showing agreement between measurements made by test and reference devices, as shown in FIG. 11A;

[0065] FIG. 11C is a scatterplot showing impedance values, measured as a function of fluid removed during hemodialysis for both the test and reference devices, where the values for the test and reference devices are relatively low;

[0066] FIG. 11BD is a correlation plot showing agreement between measurements made by test and reference devices, as shown in FIG. 11C;

[0067] FIG. 12 is a scatterplot showing the pooled impedance values for a clinical trial conducted with 33 subjects, as measured as a function of fluid removed during hemodialysis for both the test and reference devices;

[0068] FIG. 13A is a time-dependent plot of an ECG waveform measured from a patient having a normal sinus rhythm; and

[0069] FIG. 13B is a time-dependent plot of an ECG waveform measured from the same patient used to generate the waveform in FIG. 13B, only in this case the patient is experiencing ventricular tachycardia.

DETAILED DESCRIPTION

1. Monitoring ESRD Patients During Hemodialysis

[0070] As illustrated in FIG. 1, a sensor 10a-f according to the invention can be used to measure a collection of ESRD patients 11a-f connected to individual dialysis machines 13a-f. The dialysis machines, for example, may be located in a single dialysis clinic. Each sensor 10a-f, as described in more detail below with reference to FIGS. 3 and 4 below, continuously measures a collection of time-dependent physiological waveforms (ECG, TBI, PPG, PCG), vital signs (HR, RR, TEMP, SpO2, and BP) and hemodynamic parameters (TFC, SV, CO) and then wirelessly transmits data indicating these parameters to a central station 100. The sensor 10a-f typically measures waveforms at relatively high frequencies (e.g. 250 Hz) compared to the vital signs and hemodynamic parameters (e.g. once every minute). The sensor 10a-f measures the time-dependent waveforms directly from the patient with embedded sensing elements, described in more detail below. Using computational algorithms, a microprocessor within each sensor 10a-f determines the vital signs and hemodynamic parameters from the time-dependent waveforms. Examples of computational algorithms are described in the following co-pending and issued patents, the contents of which are incorporated herein by reference: NECK-WORN PHYSIOLOGICAL MONITOR, U.S. Ser. No. 62/049,279, filed Sep. 11, 2014; NECKLACE-SHAPED PHYSIOLOGICAL MONITOR, U.S. Ser. No. 14/184,616, filed Aug. 21, 2014; and BODY-WORN SENSOR FOR CHARACTERIZING PATIENTS WITH HEART FAILURE, U.S. Ser. No. 14/145,253, filed Jul. 3, 2014.

[0071] Wireless transmission is typically performed with an internal radio within the sensor 10a-f, such as a radio using protocols based on Bluetooth? or 802.11a-g (referred to herein as WiFi?). The central station 100, for example, can be a computer, workstation, tablet computer, or mobile telephone having a corresponding Bluetooth? or WiFi? radio. Alternatively, each sensor 10a-f wirelessly transmits data to a network operating within the dialysis clinic, and the central station 100 functions as a node on the network to receive the data. In yet another alternate embodiment, described in more detail with reference to FIGS. 8A, 8B, the sensor collects data during a dialysis session, and then stores it in internal memory. The data can then be sent wirelessly (e.g. to the network or central station) at a later time. For example, in the example shown in FIGS. 8A, B, the sensor wirelessly transmits data when its rechargeable battery is being charged (e.g. with a charging station). Here, the gateway is a tablet computer with an internal Bluetooth? transceiver that sequentially and automatically pairs with each sensor attached to the charging station. Once all the data collected during a dialysis session are uploaded to the gateway, the gateway then pairs with another sensor attached to the charging station and repeats the process. This continues until data from each sensor is downloaded.

[0072] A data-analytics engine 102 in communication with the central station 100 receives and processes the data (time-dependent waveforms, vital signs, and hemodynamic parameters) generated by each patient 11a-f in the dialysis clinic. More specifically, the data-analytics engine 102 is a software system that operates algorithms designed to predict decompensation of the patients 11a-f based on data generated by their respective sensor. Types of decompensation predicted by the data-analytics engine include: 1) rapid changes in vital signs or hemodynamic parameters, e.g. BP, HR, SpO2, RR, TEMP, SV, and CO; 2) hypotension or hypertension; 3) hypoxemia; 4) dysrhythmias; 5) dehydration leading to cramping; 6) chills; 7) nausea; 8) postural changes leading to ineffective therapy; 9) seizures; and 10) rapid blood loss (either internal or external). High-level algorithms for predicting these conditions are described in more detail below with respect to FIG. 2. The goal for these algorithms is to indicate to clinicians working in the dialysis clinic that a particular patient is in the early stages of decompensation, and in response generate an alarm or alert. Clinicians exposed to the alarm or alert may intervene and modify the patient's dialysis therapy to stave off the more severe decompensation before it actually occurs.

[0073] FIG. 2 shows a flow chart of an algorithm 200 that may operate on the data-analytics engine 102 shown in FIG. 1 to predict decompensation in a dialysis patient. The algorithm 200 is summarized below:

[0074] Step 202: the data-analytics engine receives the following time-dependent data from sensor: physiological waveforms (ECG, TBI, PPG, PCGsampled every 250 Hz), vital signs (HR, RR, TEMP, SpO2, BPcalculated from the waveforms every 1-15 minutes), and hemodynamic parameters (TFC, SV, COcalculated every 1-15 minutes). Vital signs and hemodynamic parameters are calculated directly on the sensor using the computational algorithms referenced above.

[0075] Step 204: the data-analytics engine calculates changes (?C) in one or more of the data from step 202, and compares values of ?C to specific threshold values (?T) determined empirically using first-principles calculations, medical knowledge, and/or prior clinical trials. Typically the values of ?T correspond to significant changes that lead to conditions such as: 1) rapid changes in BP leading to hypotension and hypertension; 2) hypoxemia; 3) dysrhythmias; 4) dehydration leading to cramping; 5) chills; 6) nausea; 7) postural changes leading to ineffective therapy; 8) seizures; and 9) rapid blood loss (either internal or external).

[0076] Step 206: compare ?C to ?T for one or more parameters collected during step 204, and determine in each case if ?C exceeds ?T.

[0077] Step 208: if ?C exceeds ?T for one or more parameter, alert clinician using an alarm (e.g. audio, visual alarm); at this point the clinician may modify the patient's ultrafiltration rate.

[0078] Step 210: if ?C does not exceed ?T for any parameter, continue dialysis therapy with the existing ultrafiltration rate.

[0079] Table 1 below describes examples of ?T values for each of the vital signs and hemodynamic parameters measured by the sensor.

TABLE-US-00001 TABLE 1 Physiological parameters measured from patients undergoing hemodialysis and corresponding values of ?T. Parameter ?T HR 20 beats/min RR 5 breaths/min TEMP 3? F. SpO2 5% BP 20 mmHg TFC 5 Ohms SV 20 mF CO 2 L/min.

[0080] In other embodiments, specific properties of the time-dependent waveforms may be processed by the data-analytics engine, which in response may trigger an alarm or alert. For example, as shown by the waveforms in FIGS. 13A,B, during dialysis the ECG waveform measured by the sensor may indicate a change from a normal sinus rhythm (FIG. 13A) to a state of ventricular tachycardia (FIG. 13B). In less severe cases, a simple change in the amplitude of a set of heartbeat-induced pulses, or a component of an individual pulse (e.g. a heartbeat-induced pulse, or a derivative thereof), within the waveform may trigger an alarm or alert. In still other embodiments, the above-described component in the waveform may be correlated to another parameter (e.g. a physiological parameter), a change in which may trigger the alarm.

2. Sensor

[0081] The sensor described herein, along with its measurements of ECG, TBI, PPG, and PCG waveforms, and its determination of HR, HRV, RR, SpO2, BP, TFC, SV, and CO values from these waveforms, is described in detail in the above-described co-pending patent applications, the contents of which have been previously incorporated herein by reference.

[0082] As illustrated in FIG. 3, a sensor 10 according to the invention is designed to monitor a patient during hemodialysis. As indicated above and explained in greater detail below, the sensor 10 measures numerical and waveform data, and then sends this information wirelessly to a central station and data-analytics engine within the dialysis clinic. The sensor 10 is typically worn around the patient's neck 28 so that it rests against their sternum, similar to a necklace or other neck-adorning jewelry. The sensor 10 features a sensing portion 30 and a securement member 32 (or securement members in an alternate embodiment, not illustrated). As illustrated, the securement member 32 extends from a first end 34 of the sensing portion 30 and attaches to a second end 36 of the sensing portion 30. The securement member 32 is long enough to pass behind the patient's neck 28 and to hold the sensing portion 30 in proper position for sensing electrodes attached to its rear, patient-facing surface to be attached to the proper locations on the patient's chest. This ensures that the sensing portion 30 is placed in approximately the same position for each measurement made on a particular patient, and that it is held in proper position to acquire the relevant bioelectric signals, as explained more fully below. Additionally, the securement member 32 houses a battery in battery compartment 38, which is positioned generally in the middle of the securement member 32 (lengthwise speaking) such that it is positioned inconspicuously behind the patient's neck 28 when the sensor 10 is worn.

[0083] In other, non-illustrated embodiments, the securement member could be split in the middle, with flexible yet shape-retaining branches extending from the first and second ends 34, 36 of the sensing portion 30 so as to pass behind the patient's neck 28, but not connect, much like a physician's stethoscope. In that case, the battery compartment could be located in one of the branches or, alternatively, in the sensing portion 30 of the sensor 10. In still further non-illustrated embodiments, a securement member might not be included, in which case attachment of the electrodes to the patient's body would, by itself, be used to hold the sensor in position.

[0084] In still other embodiments, the sensor 10 may lack the securement member 32 and only include the sensing portion 30. In this case, the system has an internal battery and resembles a patch instead of the necklace shown in FIG. 3. The patch (and corresponding sensing portion) can feature several different geometries. For example, it may be shaped like a large Band-Aid?, or have an elongated racetrack geometry. The patch may be work near the center of the patient's chest, as shown in FIG. 3, or on the left or right-hand side of the chest.

[0085] The sensing portion 30 is typically constructed in two or more sections or segments, e.g. a central segment 42 and two outboard segments 40a and 40b. Electrode patches attach to the rear of the two outboard segments 40a and 40b, as described below. The segments are connected to each other by means of flexible connector segments (not shown in the figure), which in turn are encased in flexible housing 46 and 48. The flexible connector segments are typically made from a polymeric material, e.g. Kapton? flexible printed circuits available from the DuPont Corporation. Such materials are essentially a flexible, polymeric film that encases one or more thin conducting members, which are typically made from copper. Each of the segments 40a, 40b, and 42 includes, respectively, a rigid circuit board (not shown in the figure) populated with discrete electrical circuit components, described in more detail below. The rigid circuit boards connect to one another via the flexible connector segments, which each include 20 conductive members.

[0086] The rigid circuit boards are each encased inside of a rigid protective housing segments 53a, 53b, 55, and the flexible connector segments are encased within the flexible connector segments 46 and 48. The protective housing segments 53a, 53b, and 55 are more typically made from opaque plastic, which contributes to the overall aesthetically pleasing appearance of the sensor 10. Suitably, the connector segments 46 and 48, which may be formed as rubber boots designed to snap into respectively opposing ends of the protective housing segments 53a, 53b, 55, are typically made from soft, flexible material such as silicone rubber. Generally speaking, such a configuration of the sensing portion 30 serves to hold the sensing electrodes at their proper positions before they are adhered to the patient's chest, while allowing the sensing portion 30 to conform to the different curvatures of the physiological region upon which it rests.

[0087] A transparent or translucent plastic window 57 located on the top, anteriorly facing surface of central housing segment 55 covers an underlying LED, which serves as a simple user interface for the patient 12. For example, the LED can radiate different colors of the visible spectrum, and blink them at different frequencies, to indicate when the sensor 10 is turned on, making a measurement, charging, running on low power, completed with a measurement, etc. Additionally included in the sensor are an acoustic buzzer and/or vibrating component. Collectively, the LED, buzzer, and vibrating component can alert the clinician in case of an alarm, triggered as described above.

[0088] As shown in FIGS. 4A and 4B, on its rear-facing surface 101 the sensor 10 includes a pulse oximetry sensor 100 that operates using reflection-mode optics, and an acoustic sensor 103 featuring a piezoelectric microphone that measures sounds generated when valves close in the patient's heart. The pulse oximetry sensor 100 and acoustic sensor 103 generate, respectively, PPG and PCG waveforms during a measurement, as described in more detail below. Such waveforms can be further processed to determine SpO2, BP, SV, CO and other parameters. The pulse oximetry sensor 100 and acoustic sensor 103 are disposed on the back surface of opposing housing segments (53a and 53b in FIG. 3), between magnetic interfaces for sense 105a,b and drive 107a,b electrodes associated with the ECG and impedance circuits. During a measurement, as indicated in FIG. 4B, stainless steel posts embedded within two separate electrode patches 109a, 109b connect to the magnetic interfaces for sense 105a,b and drive 107a,b electrodes. Each electrode patch 109a, 109b includes an aperture (i.e. a cut-out circular hole) so that the underlying sensing element (pulse oximetry sensor 100 and acoustic sensor 103) can directly contact the patient's skin when the sensor is worn. Measurements are then made as described in more detail below.

[0089] The above-described patent applications, which have been incorporated herein by reference, describe how the sense and drive electrodes measure both ECG and TBI waveforms. To summarize, the drive electrodes inject high-frequency, low-amperage current into the patient's chest. The sense electrodes sense a voltage that indicates the impedance encountered by the injected current. The voltage is passed through a series of electrical circuits featuring analog filters and differential amplifiers to filter out and amplify signal components related to the two different waveforms. This is done using techniques known in the art, and described in the patent applications. One of the signal components indicates the ECG waveform. Another indicates the TBI waveform. The TBI waveform has low-frequency and high-frequency components that are further filtered out and processed, as described in more detail below, do determine different impedance waveforms.

[0090] An example of a pulse oximetry sensor is described in U.S. Pat. No. 8,437,824, the contents of which are incorporated by reference in their entirety. The pulse oximetry sensor 100 drives red and infrared LEDs in an alternating, pulsatile manner and controls a light-sensitive, photodetector diode, as generally known in the art. It is configured to operate in a reflection mode, meaning that the LEDs and light-sensitive diode are positioned so as to receive radiation from the same direction. It measures PPG waveforms from capillary beds in the patient's chest to generate a value of SpO2. This is in contrast to conventional pulse oximetry sensors in which the LEDs and the light-sensitive diode are positioned across from each other, with a space into which fits a body part (e.g., a finger or an earlobe) being located between the LEDs and the light-sensitive diode. Thus, the pulse oximetry circuit detects and measures radiation emitted by the diodes that has been reflected off of capillary beds (i.e., in the chest) before arriving at the light-sensitive diode.

[0091] The acoustic sensor 103 typically includes a microphone (e.g. a piezoelectric microphone) and amplifier system, and is designed to detect a PCG waveform indicating heart sounds, primarily caused by the closings of the atrioventricular and semilunar valves during each heartbeat. Alternatively, a sensitive accelerometer can be used in place of the acoustic sensor 103 to measure small-scale, seismic motions of the chest driven by the patient's underlying beating heart. Such waveforms are referred to as seismocardiogram (SCG) and can be used in place of (or in concert with) PCG waveforms.

[0092] Because both the pulse oximetry sensor 100 and acoustic sensor 103 are incorporated into the overall sensor 10, they can connect comfortably to the patient's chest to measure signal in an effective manner that eliminates cable clutter and frees the patient's hands and fingers (where pulse oximetry measurements typically are taken) for other purposes. An additional benefit of this configuration is reduction of motion artifacts, which can distort PPG waveforms and cause erroneous values of SpO2 to be reported. This reduction of motion artifacts is due to the fact that during everyday activities, the chest typically moves less than the hands and fingers, and subsequent artifact reduction ultimately improves the accuracy of parameters measured from the patient.

[0093] FIGS. 5A-E shows time-dependent plots of ECG, TBI, PPG, and PCG waveforms measured by the sensor according to the invention, along with x symbols indicating fiducial points in the waveforms determined by feature-detecting firmware operating on the sensor. As described in detail below, the sensor measures a collection of physiological signals by collectively processing all four waveforms.

[0094] BP, including SYS and DIA, is particularly relevant for ESRD patients, as they can easily enter into hypertensive and (more commonly) hypotensive states during hemodialysis treatments. Measurement of BP with the sensor is therefore discussed in more detail here. The sensor monitors BP by simultaneously tracking the physiologic waveforms shown in FIGS. 5A-E. The ECG waveform shown in FIG. 5A includes a heartbeat-induced QRS complex that informally marks the beginning of each cardiac cycle. Following this is a PCG waveformcaptured with the acoustic sensor and shown in FIG. 5Bindicates heart sounds. Following this is a PPG waveformcaptured with the pulse oximetry sensor and shown in FIG. 5Cthat monitors volumetric changes in underlying capillaries. The TBI waveform includes DC (Z.sub.0) and AC (?Z) components: Z.sub.0 senses the amount of fluid in the chest by measuring underlying electrical impedance and represents the baseline of the waveform; AZ tracks blood flow in the thoracic vasculature and represents the pulsatile components of the waveform (as shown in FIG. 5D).

[0095] The QRS complex provides a fiducial marker to delineate each heartbeat. Feature-detection algorithms operating in the sensor calculate time intervals between the QRS complex and fiducial markers on each of the other waveforms. For example, the time separating a foot of a pulse in the PPG waveform and the QRS complex is referred to as PAT. PAT relates to BP and systemic vascular resistance. During a measurement, the sensor calculates PAT, along with VTT and other time-dependent parameters extracted from the four physiologic waveforms (collectively referred to below as INT). Additionally, the sensor calculates information about the amplitudes of heartbeat-induced pulses in some of the waveforms (AMP). For example, the amplitude of the pulse in the derivative of the AC component of the TBI waveform (dZ/dt.sub.max), as shown in FIG. 5E, indicates the volumetric expansion and forward blood flow of the thoracic arteries, and is related to SYS and the contractility of the heart.

[0096] The general model for calculating SYS and DIA involves measuring a collection of INT and AMP values from the four physiologic waveforms. FIGS. 6A-6E, for example, show a collection of INT values that may correlate to BP. These include: 1) EMAT, shown in FIG. 6A, which is the time separating an ECG QRS and the onset of the S1 heart sound; 2) VTT.sub.1 (FIG. 6B) which is the time separating the onset of pulses in the TBI and PPG waveforms; 3) VTT.sub.3 (FIG. 6C) which is the time separating the onset of the S1 heart sound and the onset of a pulse in the PPG waveform; 4) PAT (FIG. 6D) which is the time separating the ECG QRS and the onset of a pulse in the PPG waveform; 5) VTT.sub.2 (FIG. 6E) which is the time separating the onset of the S1 heart sound and the onset of a pulse in the TBI waveform; and 6) LVET (FIG. 6F) which is the time separating the S1 and S2 heart sounds. Note from Eqn. 3 above, LVET is typically estimated directly from a cardiac pulse in the TBI waveform, or from the patient's current HR using Weissler's regression. Errors in these estimations may lead to errors in calculating SV. Determining LVET directly from S1 and S2 may reduce such errors, and thus improve the accuracy of the calculated SV.

[0097] Once these parameters are determined, firmware on the sensor then collectively processes them, along with demographic information (e.g. age and gender) and information measured during a patient-specific calibration described below with reference to FIGS. 7A and 7B, to determine BP values without requiring a cuff. Eq. 5 below, for example, shows one example of an algorithm (e.g. an equation) for determining BP from the parameters shown in FIGS. 6A-F. In the equation, coefficients a-f are determined during the calibration.

BP=a?EMAT+b?VTT.sub.1+c?VTT.sub.3+d?PAT+e?VTT.sub.2+f?LVET(5)

[0098] This allows, for example, BP values (SYS and DIA) to be monitored in a quasi-continuous manner (e.g. every minute or so) during hemodialysis, thereby allowing detection of rapid excursions into hypertensive and hypotensive states.

[0099] The sensor according to the invention also typically includes a three-axis digital accelerometer and a temperature sensor (not specifically identified) to measure, respectively, three time-dependent motion waveforms (along x, y, and z-axes) and TEMP values.

3. Measurement of Stroke Volume

[0100] According to the invention, algorithms for calculating SV and other physiological parameters (e.g. BP, SpO2, RR, CO) are described in more detail in the co-pending patent applications described above, the contents of which have been previously incorporated herein by reference. The algorithms described therein can be improved upon by collective processing of time-dependent TBI and PCG waveforms, as indicated in FIG. 6. The figure shows a plot of pulses induced by a single heartbeat for these waveforms. To measure SV, a microprocessor within the sensor first processes the PCG waveform, and specifically the temporal delay between the S1 and S2 heart sounds therein (as indicated in FIG. 6F), to determine LVET, which is then used in various forms of the SV equation, as described in the above-referenced patent applications, to determine SV. Once determined, SV is further processed to determine PP, which is then further processed to determine SYS and DIA. The invention also includes secondary algorithms for converting SV into CO, and processing S1 and S2 to determine a patient's cardiac function.

[0101] In alternative embodiments, the invention may include use a signal-processing technique called beatstacking to improve the signal-to-noise ratio of heartbeat-induced pulses in the TBI waveform. With beatstacking, an average pulseZ(t)is calculated from multiple (e.g. seven) consecutive pulses from the TBI waveform, which are delineated by an analysis of the corresponding QRS complexes in the ECG waveform, and then averaged together. The derivative of Z(t)dZ(t)/dtis calculated over an 8-sample window. The maximum value of Z(t) is calculated, and used as a boundary point for the location of [dZ(t)/dt].sub.max. This parameter is used directly in the SV equation, described above.

4. Calibrating the Blood Pressure Measurement

[0102] Measurement of BP made by the sensor during dialysis must be calibrated with a cuff-based system. A preferred approach, illustrated in FIGS. 7A and 7B, uses a BP calibration device that features a cuff-based oscillometric measurement. The BP calibration device is typically included directly in the machine used for hemodialysis. Alternatively it can be included in an off-the-shelf device separate from the dialysis machine. Calibration is typically performed at the start of each dialysis session. As shown in FIGS. 7A and 7B, it requires a sensor 10 disposed on the chest of a patient 12, and a third disposable patch electrode 17, identical to the 2-part electrodes that attach directly to the sensor's base and shown in FIG. 4B, which adheres to a patient's wrist or forearm. During a calibration, the third electrode 17 connects through a thin cable 19 (about 3 feet in length) to the sensor's base. A BP cuff 21 associated with the BP calibration device is placed on the same arm as the third electrode.

[0103] Calibration begins when the user (either a patient or clinician) presses a button labeled Calibration on a user interface of the tablet computer gateway (not shown in the figure). The user interface asks for input of certain biometric parameters corresponding to the patient (e.g. age, gender), and then prompts the user to initiate an oscillometric BP measurement with the BP calibration device. This process establishes a Bluetooth? connection between the gateway and the BP calibration device. The Sensor then begins to measure PCG and PPG waveforms from the patient's chest, and ICG and ECG waveforms between the third electrode on the patient's wrist/forearm and one of the electrodes adhered to their chest. Over Bluetooth?, the BP calibration device transmits DC (PRES-DC) and AC (PRES-AC) pressure waveforms to the sensor. These represent, respectively, the background pressure that the cuff 21 applies to the patient's brachial artery during the measurement and the oscillometric envelope. Algorithms within the sensor 10 synchronize its four waveforms with the PRES-DC and PRES-AC waveforms measured by the BP calibration device. Upon completion of the oscillometry measurement, the BP calibration device also transmits initial BP values (SYS.sub.0, DIA.sub.0, and MAP.sub.0) to the sensor.

[0104] Firmware within the sensor then collectively processes the waveforms with a computational model to determine the first component of the calibration: a patient-specific relationship between INT, AMP and changes in BP. These are indicated by coefficients a-f, shown above in Eq. 5. The second component of the calibration is SYS.sub.0, DIA.sub.0, and MAP.sub.0. Collectively these two components represent a calibration, which holds for the entire dialysis session. Once the sensor calculates the calibration, it notifies the gateway over Bluetooth?, which prompts the patient to remove both the third electrode and the BP cuff. Cuffless measurements of BP can then commence with the sensor.

5. Charging Sensors and Downloading Information after a Dialysis Session

[0105] FIGS. 8A and 8B show a collection of sensors 210 attached to a charging station 211. The charging station, for example, may be used in a dialysis clinic to charge the sensor between dialysis sessions. Each sensor 210 is powered by a rechargeable Li:ion battery 217, which as described above is located in its securement member or cable 219. During a measurement, the cable 219 loops around the neck of a patient so that the battery 217 is tucked behind their neck. The sensor 210 is powered on when its clasp 221 snaps into a mated magnetic interface 218 on the sensor's base 223. This action completes a power circuit within the sensor, causing it to power on. Measurements then commence. Before or after a dialysis session, a large number of sensors 210 may require charging of their Li:ion batteries 217. A charging station 211 including multiple ports 212 connects to the sensors 210 to charge their batteries 217. The charging station 211 is plugged into a mains outlet through a plug. Each port 212 on the charging station 211 includes a magnet and a plastic component designed to mate with the clasp 221 (and magnetic interface 218) of each sensor 210. When the clasp 221 is connected to the charging station 211, wires in the cable 219 connect each sensor's battery 217 to a corresponding port 212. Power from the mains outlet then charges the battery 217.

[0106] As indicated in FIG. 8B, during the charging process data collected from a set of patients undergoing dialysis can be automatically downloaded from each sensor, and then forwarded to a central gateway or cloud-based system for follow-on analysis. For example, during charging, a tablet computer gateway 221 can be placed proximal to the charging station so that its internal Bluetooth? transceiver is within range of corresponding Bluetooth? transceivers within each sensor. The tablet computer gateway 221 can run a software program featuring a customized user interface 222 that automatically locates each Bluetooth? transceiver within each sensor, pairs with it, and the downloads the data collected during dialysis and stored on internal memory within the sensor. Once data is collected from one sensor, the tablet computer gateway finds a subsequent sensor, and repeats the downloading process. This continues until data are downloaded from each sensor. The tablet computer gateway 221 then forwards the downloaded data to a secondary computer system, e.g. a web-based computer system, for follow-on analysis.

6. Clinical Results

[0107] A clinical study with patients undergoing hemodialysis was performed using the sensor described herein, and clearly demonstrates its ability to measure some of the above-described parameters, and particularly TFC. During the study, the sensor (referred to below as the test device) measured TFC as described above, and measurements of Z.sub.0 (a parameter related to the inverse of TFC) were made with a second reference device, the Cardiodynamics BioZ. FIGS. 9A and 9B show the electrode positions for these two devices. Circle 300 in FIG. 9B shows the BioZ's electrode position, indicating its measurement of Z.sub.0 represents an impedance value for the entire thoracic cavity. Physiological components such as blood, bone, and thoracic fluids, contribute to its value. In contrast, the test device's measurement of TFC, shown by circle 301 in FIG. 9A, represents an impedance value from a localized and relatively small region near the sternum. For this reason, when deployed on a patient, the test device's measurement of TFC and its associated sensitivity to fluid changes are expected to have lower values than those from corresponding measurements made by the reference device. All measurements were made on patients undergoing hemodialysis.

[0108] The test device uses only four electrodes, as compared to eight for the reference device. Two electrodes in the test device inject current for the bioimpedance measurement, compared to four electrodes for the reference device. Both the test and reference devices inject a high-frequency, low-amperage current: for the test device the frequency is 100 KHz and amperage is about 6 mA, compared to about 70 KHz and 4 mA for the reference device. Like the reference device, the test device measures both AC and DC waveforms, with its TFC value representing a 30-second average of the DC waveform.

[0109] Table 2 summarizes data collected from each subject in the first cohort, and includes: 1) BIAS and STDEV between test and reference device; 2) correlation between measurements made by test and reference devices; 3) correlation between measurements made by test/reference devices and the amount of fluid removed during dialysis; and 4) sensitivity (i.e. a slope with units of Ohms/L) of measurements made by both test and reference devices. The last row in the table shows an average of all these values. Here, the sensitivity accounts for intravenous saline disposed into the subject during dialysis; this value was typically 500 mL.

TABLE-US-00002 TABLE 2 Summary of statistics for the clinical study performed with test and referenced devices. r - r - BIAS STDEV correlation r - correlation (test device, (test device, (test device, correlation (reference sensitivity reference reference reference (test device, device sensitivity (reference device, device, device, fluid removed, fluid removed, (test device, device, Subject units Ohms) units Ohms) no units) no units) no units) units Ohms/L) units Ohms/L) 300 4.56 0.88 0.62 0.82 0.88 1.08 1.08 301 14.07 0.83 0.93 0.98 0.97 1.38 1.95 303 8.30 1.04 0.89 0.94 0.97 1.92 2.64 304 5.33 1.39 0.84 0.80 0.96 2.40 1.80 306 21.35 2.70 0.68 0.88 0.90 1.03 2.51 307 6.93 0.82 0.92 0.91 0.97 0.48 1.56 309 6.81 0.72 0.87 0.90 0.87 0.68 1.02 310 16.95 1.03 0.89 0.94 0.91 1.46 2.43 311 10.78 0.99 0.95 0.96 0.93 0.77 1.33 314 8.97 1.62 0.87 0.88 0.99 2.12 3.41 316 2.63 0.67 0.66 0.95 0.84 1.54 1.02 318 5.38 0.50 0.97 0.98 0.99 3.10 3.10 319 11.95 1.39 0.89 0.98 0.90 1.92 3.00 321 5.76 0.99 0.67 0.96 0.82 2.20 1.68 322 5.79 1.37 0.97 0.99 0.97 3.16 2.20 323 3.65 0.61 0.94 0.94 0.99 3.27 3.27 324 10.02 0.91 0.88 0.91 0.95 2.18 2.05 326 15.18 0.45 0.94 0.95 0.95 1.39 1.90 329 12.12 1.11 0.90 0.97 0.95 2.62 2.02 331 10.87 1.24 0.93 0.93 0.99 1.76 2.21 332 7.75 0.72 0.92 0.98 0.89 4.34 2.89 335 3.73 0.38 0.73 0.54 0.81 1.13 1.46 337 4.98 0.80 0.75 0.84 0.94 1.30 1.18 338 5.12 0.72 0.96 0.95 0.99 0.57 1.15 339 12.44 0.94 0.93 0.94 0.99 0.88 1.43 340 4.02 0.85 0.91 0.96 0.96 1.50 1.04 342 14.61 1.25 0.88 0.95 0.89 1.85 4.26 343 9.09 1.07 0.84 0.98 0.91 1.00 1.23 344 8.70 0.99 0.88 0.88 0.95 0.82 1.75 345 16.71 1.27 0.88 0.84 0.94 1.26 2.06 346 7.84 1.86 0.77 0.91 0.93 2.87 5.08 348 16.32 0.94 0.91 0.92 0.99 2.08 2.93 349 4.56 0.61 0.69 0.88 0.92 0.88 1.25 AVE 9.19 1.02 0.86 0.91 0.93 1.72 2.12

[0110] From the data shown in Table 2, it is noted that the average sensitivities for both test (1.68 Ohms/L) and reference (2.02 Ohms/L) devices are similar to those calculated with a mixed-effects statistical model (1.69 Ohms/L and 1.88 Ohms/L, respectively).

[0111] FIGS. 10A-D, 11A-D, below, show plots from 4 subjects (314, 322, 331, 302) indicating the dependence of impedance measurements made by the test and reference devices with fluid removed during dialysis (FIGS. 10A, 10C, 11A, 11C). Corresponding plots (FIGS. 10B, 10D, 11B, 11D) are standard correlation plots showing the agreement between measurements made by the two devices.

[0112] Data from the four subjects described above is shown because of the disparate way their impedance values relate to fluid removed. For example, impedance values for subject 314 (FIGS. 10A,B) diverge with increased amounts of fluid removed, while those from subject 322 (FIGS. 10C,D) converge. Likewise, impedance values for subject 331 (FIGS. 11A,B) are some of the highest values recorded, while those for subject 302 (FIGS. 11C,D) are some of the lowest. In all cases, the fluid-dependent plots indicate a strong linear relationship between the impedance values from both test and reference devices and the amount of fluid removed. The correlation plots indicate there is also strong linear relationship between values measured by the two devices.

[0113] Importantly, these data clearly appear to show that bioimpedance measurements made by the test device from a relatively small region on the sternum (e.g. circle 301 in FIG. 9A) are sensitive to a patient's fluid variations throughout their thoracic cavity (e.g. circle 300 in FIG. 9A). As expected, bioimpedance values measured from this region have a lower overall value (average BIAS of 9.16 Ohms) and sensitivity (average 1.69 Ohms/L) to fluid changes compared to those measured from the entire thoracic cavity (average 1.88 Ohms/L). This is because measurements from the sternum sample a relatively small physiological area with a correspondingly low fluid volume. However, as shown in Table 2, during periods of fluid removal a strong linear relationship was observed between test and reference devices in 100% of the subjects. Likewise, in 100% of these subjects, measurements made by the test device showed a strong linear relationship and sensitivities to the amount of fluid removed during dialysis.

[0114] For the study described above, averaged values measured from subjects with ESRD alone (i.e. cohort 1A with 23 total subjects) were compared to those with both ESRD and CHF (cohort 1B with 10 total). Table 3, below, summarizes these results.

TABLE-US-00003 TABLE 3 Averaged statistical values for cohort 1A and 1B. AVE r - AVE r - correlation AVE r - correlation AVE AVE BIAS AVE STDEV (test device, correlation (reference AVE sensitivity (test device, (test device, reference (test device, device, sensitivity (reference reference reference device, fluid removed, fluid removed, (test device, device, Cohorts units Ohms) units Ohms) no units) no units) no units) units Ohms/L) units Ohms/L) 1A 8.48 1.01 0.85 0.91 0.93 1.67 1.89 1B 10.82 1.05 0.89 0.93 0.94 1.69 2.31

[0115] As is clear from the table, subjects with both CHF and ESRD show a larger average BIAS between test and measurement devices, as well as a larger sensitivity for both test and reference devices, than those with ESRD alone. This is presumably related to these subjects' diagnosis of CHF, which means they typically have larger amounts of thoracic fluids distributed throughout the body. However the table indicates that the overall measurement performance of the test device is essentially the same for the two cohorts.

[0116] Using the results from the clinical study, the relationship between the test device's TFC value and the reference device's Z.sub.0 value was evaluated with a repeated-measures model. To perform this analysis, a repeated-measures model (which accounts for correlations between successive points in time with an auto-regressive AR(1) term) was used to fit the data, with: Model Aseparate slopes for each subject; and Model Ba common slope for all subjects. The results for the two models was then compared to the fit of the two models using the AIC, which is a measure of the relative quality of a statistical model for a given set of data. The results are as follows:

Model A (separate slopes): AIC=1307.3
Model B (same slope): AIC=1400.9

[0117] This indicates that separate, subject-specific slopes should be used to compare changes in impedance throughout the entire thoracic cavity (as measured, e.g., with the reference device) with changes in impedance in an isolated region of the sternum (measured, e.g., with the test device).

[0118] To further analyze the data, a similar repeated-measures model was used to investigate the relationship between the test device's TFC value and fluid removed (?F). This analysis was conducted similarly to that described above. Specifically, TFC values measured by the test device were modeled as a linear function of: 1) fluid removed; 2) using a subject-specific y-intercept term; and 3) using an autocorrelation term that accounts for the dependence of temporally sequential measurements. The following models were then used to fit data corresponding to all subjects to test the equality of slopes: Model Aseparate slopes for each subject; and Model Ba common slope for all subjects. As with the analysis described above, the model with the smallest AIC was chosen as the best fitting model. Results for this analysis are shown below:

Model A (separate slopes): AIC=1636.0
Model B (same slope): AIC=1241.7

[0119] This indicates that a single (i.e. common) slope can be used to compare changes in fluid in an isolated region of the sternum with changes in impedance from that same region (measured, e.g., with the test device). More specifically, Model B predicts that each subject starts (at time 0) with a unique TFC value, and that for each liter of fluid removed, the TFC value will increase by approximately 1.5 Ohms, i.e. a sensitivity of 1.5 Ohms/L. This model doesn't account for intra-venous saline introduced into each subject (500 mL) during the dialysis period. When this is accounted for, the sensitivity increases to 1.69 Ohms/L. This is essentially identical to the average sensitivity (1.68 Ohms/L) shown above in Table 2.

[0120] To further investigate the above, the percentage change of impedance for both the test (TFC) and reference (Z.sub.0) devices was investigated. Here, the percentage change in impedance was calculated by first pooling all subject-specific data collected during the clinical study, and then determining an average value of both TFC and Z.sub.0 for each 200 mL of fluid removed. The percentage change was the average value of impedance at these 200 mL increments divided by the average value of impedance before dialysis was started. For this calculation, only a few subjects had more than 3 L of fluid removed. Thus the average values of TFC and Z.sub.0 above this level reflect data collected from only a few subjects, whereas the average values below this level reflect data collected from a relatively large number of subjects. For example, the impedance value at 4 L of fluid removed is the average of just 2 samples, while that at 2 L removed is the average of 23 samples.

[0121] FIG. 12 shows the percentage change of TFC and Z.sub.0 values, calculated as described above, plotted as a function of fluid removed. The curves show nearly identical trajectories, indicating that for dialysis patients the percentage change of fluid measured from a relatively isolated region near the sternum (i.e. TFC) is nearly identical to that measured from the entire thoracic cavity (i.e. Z.sub.0). As described above, the relatively low number of samples at high volumes of fluid removed may explain scatter in the data above 3 L.

[0122] Percentage change TFC and Z.sub.0 values from FIG. 12 were than compared directly using standard correlation plots. The plots indicate strong agreement between the two data sets (r.sup.2=0.88, r=0.94) and a slope (0.91) near unity, again supporting the claim that the percentage change of fluid measured from the sternum is nearly identical to that measured from the entire thoracic cavity.

[0123] Using the sensor as described herein, parameters other than TFC can be detected from patients undergoing hemodialysis, and used to characterize their progression towards decompensation. For example, as shown in FIGS. 13A, B, ECG waveforms measured by the sensor can indicate a normal sinus rhythm (FIG. 13A), or in contrast an abnormal sinus rhythm such as ventricular tachycardia (FIG. 13B). Algorithms operating on the data-analytics engine can analyze the ECG waveforms to detect these excursions, and then notify a clinician using an alarm/alert. This may drive the clinician to pause the dialysis therapy.

[0124] Data generated by the sensor may indicate other in-dialysis conditions as well. These include 1) rapid changes in BP leading to hypotension and hypertension; 2) hypoxemia; 3) dysrhythmias; 4) dehydration leading to cramping; 5) chills; 6) nausea; 7) postural changes leading to ineffective therapy; 8) seizures; and 9) rapid blood loss (either internal or external).

[0125] In other embodiments, multiple physiological parameters measured by the sensor (e.g. TFC, BP, SV) may be lumped together into a single figure of merit or index, and used to characterize a patient undergoing dialysis.

[0126] These and other embodiments of the invention are deemed to be within the scope of the following claims.