MINIATURE PIEZOELECTRIC CARDIOVASCULAR MONITORING SYSTEM

• US 20150305632A1
• Filed: 03/31/2015
• Published: 10/29/2015
• Est. Priority Date: 03/31/2014
• Status: Active Grant

• Current Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Original Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Inventors: Najarian, Kayvan, Oldham, Kenn, Ward, Kevin R., Slavin, Daniel, Belle, Ashwin, Ansari, Sardar, Daniels, Rodney C.
• Granted Patent: US 10,610,113 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0261 Strain gauges
• CPC Class Codes: A61B 2562/06 Arrangements of multiple sensors of different types
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00

Abstract

The invention is a passive, wearable sensor that uses a thin piezoelectric material to produce a time history of blood pressure of the patient, with signal processing algorithms to extract physiological information. The sensor consists of a piezoelectric transducer set in a polymer laminate that can be applied to the finger or wrist of the patient. During use, a combination of compressive and bending deformation in the piezoelectric layer in response to blood pressure in the finger or wrist as a voltage output. Using signal processing techniques, the raw signal is filtered and decomposed to obtain a information to form derivative signals such as blood pressure, pulse pressure, pulse pressure variability, heart rate, heart rate variability, and respiratory rate which can be very important pre-cursors in the monitoring of the patient'"'"'s physiological conditions.

Claims

1. An apparatus comprising:

a wearable sensor assembly including a flexible band having a polymer layer and a sensing layer both positioned for mounting the wearable sensor onto a sensing region of a subject for measuring vascular wall motion and blood flow dependent measurements over the sensing region, the wearable sensor comprising;

(i) a piezoelectric sensor for measuring raw signal data, in real time, of vascular wall motion and blood flow dependent measurements, wherein the piezoelectric sensor comprises a piezoelectric electrode structure in the sensing layer for measuring the raw signal data in response to physical movement of the sensor region as detected by the piezoelectric sensor, and

(ii) a secondary sensor for collecting and extracting photoplethysmograph derived blood flow data and photoplethysmograph derived waveform features; and

a signal processor configured to

receive the raw signal data from the piezoelectric sensor,

filter the received raw signal data from the piezoelectric sensor,

perform signal decomposition on the filtered raw signal data from the piezoelectric sensor,

analyze the received raw signal data from the piezoelectric sensor to extract one or more waveform features from the received raw signal data, and

analyze the photoplethysmograph derived blood flow data and the photoplethysmograph derived waveform features from the secondary sensor and compare the analyzed blood flow data and the waveform features to the extracted one or more waveform features from the piezoelectric sensor to extract indicators of circulating vascular volume and/or vascular tone to characterize and/or predict vascular health of the subject for clinical decision making.

2. The apparatus of claim 1, wherein the wearable sensor assembly further comprises a motion sensor device for measuring motion data of changes in motion of the wearable sensor in response to changes in the location or orientation of the sensing region and/or of the subject, wherein the wearable sensor transmits the motion data to the signal processor.

3. The apparatus of claim 2, wherein the signal processor is further configured to cancel noise in the raw signal data based on the motion data.

4. The apparatus of claim 2, wherein the motion sensor is a gyroscopic sensor or an accelerometer.

5. The apparatus of claim 1, wherein the signal processor is configured to compare the analyzed blood flow and photoplethysmograph derived waveform features to the extracted one or more waveform features from the piezoelectric sensor by:

determining a first set of one or more local peaks and waveform features in the received raw signal data from the piezoelectric sensor;

determining a second set of one or more local peaks and waveform features in the blood flow data from the secondary sensor; and

determining a peak and waveform feature difference profile between the first set and the second set.

6. The apparatus of claim 1, wherein the wearable sensor further comprises a temperature sensor, motion sensor, actigraphy sensor, galvanic skin response sensor, impedance sensor, or a combination thereof.

7. The apparatus of claim 1, wherein the piezoelectric sensor comprises a polymer outer layer sandwiching two piezoelectric electrodes to form the piezoelectric electrode structure in the sensing layer.

8. The apparatus of claim 1, a hard backing layer for mounting the wearable sensor against the sensing region, wherein wearable sensor is coextensive with the hard backing layer, the hard backing layer defines an inner sensing surface and an outer mounting surface.

9. The apparatus of claim 8, further comprising a communication interface mounted to the outer mounting surface of the hard backing layer, wherein the communication interface is either a wireless or a wired communication interface.

10. The apparatus of claim 8, further comprising a self-contained power source of the piezoelectric sensor and/or the photoplethysmography sensor mounted to the hard backing layer.

11. The apparatus of claim 1, wherein the one or more extracted waveform features from the piezoelectric sensor are expressive of one or more physiological features selected from the group consisting of blood pressure (BP), pulse pressure (PP), pulse pressure variability (PPV), heart rate (HR), heart rate variability and complexity (HRV), arterial wall stiffness (AWS), blood flow (BF), blood volume (BV), pulse transit time (PTT), and respiratory rate (RR) changes in the sensing region over a sample time frame.

12. The apparatus of claim 1, wherein the extracted waveform features from the secondary sensor are expressive of one or more of physiological features selected from the group consisting of (BP), pulse pressure (PP), pulse pressure variability (PPV), heart rate (HR), heart rate variability and complexity (HRV), arterial wall stiffness (AWS), blood flow (BF), blood volume (BV), pulse transit time (PTT) and respiratory rate (RR) are used to create early warning physiologic indices reflective of changes in human health as well as indices used for clinical decision support to restore health.

13. The apparatus of claim 12, wherein extracted waveform features and indices are measured in response to applied physiological changes.

14. The apparatus of claim 13, wherein the applied physiological changes comprise temperature changes produced by external stimuli or pressure changes resulting from at least one of blood flow occlusions proximal to the wearable sensor assembly, Valsalva maneuvers, leg lift maneuvers, or change in breathing maneuvers.

15. The apparatus of claim 1, wherein the signal processor is external to the wearable sensor assembly.

16. A therapeutic delivery system for administering a therapeutic treatment to a subject, the delivery system comprising:

the apparatus of claim 1; and

an administration system comprising a therapeutic delivery vehicle in communication with a therapeutic treatment processor that controls delivery of the therapeutic treatment in response to received patient status data, the therapeutic treatment processor containing the signal processor and

(i) coupled to receive the raw signal data from the piezoelectric sensor, in a closed loop manner,

(ii) implemented to store the one or more extracted waveform features of the sensing region in the patient status data, and

(iii) implemented to determine instructions for administering the therapeutic treatment, in response to the stored one or more extracted waveform features.

17. A therapeutic delivery system for administering a therapeutic treatment to a subject, the delivery system comprising:

the apparatus of claim 1; and

an administration system comprising a therapeutic delivery vehicle in communication with a therapeutic treatment processor that controls delivery of the therapeutic treatment in response to received patient status data, the therapeutic treatment processor

(i) coupled to the signal processor to receive the one or more extracted waveform features of the sensing region,

(ii) implemented to store the one or more extracted waveform features of the sensing region in the patient status data, and

(iii) implemented to determine instructions for administering the therapeutic treatment in response to the stored one or more extracted waveform features.

18. The apparatus of claim 1, wherein the signal processor is further configured to:

receive, in real-time, the raw signal data from the piezoelectric sensor;

suppress motion artifacts from the received the raw signal data from the piezoelectric sensor based on data from one or more motion sensors in the sensor assembly;

extract at least one of time-based or transform-based features from the raw signal data; and

predict, based on an input of the at least one of the time-based or the transform-based features to a machine learning prediction model, one or more targeted physiologic events.

19. The apparatus of claim 1, wherein the wearable sensor assembly is integrated into a wristband device, the wristband device configured to monitor health and activity.

20. The apparatus of claim 1, wherein the wearable sensor assembly is integrated into a head-mounted device, the head-mounted device configurable to monitor health and activity.

21. The apparatus of claim 1, wherein the wearable sensor assembly is integrated into a device wearable by a patient on at least one of an arm, wrist, hand, finger, ankle, foot, toe, leg, head, neck, chest, or waist.

22. The apparatus of claim 1, wherein the signal processor is further configured to:

receive, in real-time, the raw signal data from the piezoelectric sensor;

suppress motion artifacts from the received the raw signal data from the piezoelectric sensor based on data from one or more motion sensors in the sensor assembly;

extract at least one of time-based or transform-based features from the raw signal data; and

determine whether a physiological or monitoring alarm is a true alarm or a false alarm.

23. The apparatus of claim 22, wherein the physiological alarm is generated by at least one of pulse oximetry, arterial blood pressure monitoring, respiration monitoring, or ECG monitoring.

24. The apparatus of claim 1, wherein the signal processor is configured to:

generate (i) a health report and/or (ii) alarm condition of a morphology of the sensing region of a subject and correlated to either vascular volume, vascular tone, the one or more features, or a combination thereof.

25. The apparatus of claim 1, wherein the signal processor is configured to:

generate (i) a health report and/or (ii) alarm condition of a predicted morphology of the sensing region of a subject and correlated to either vascular volume, vascular tone, the one or more features, or a combination thereof.