System and method for infrasonic cardiac monitoring

• US 10,542,961 B2
• Filed: 06/14/2016
• Issued: 01/28/2020
• Est. Priority Date: 06/15/2015
• Status: Active Grant

• Current Assignee: The Research Foundation for The State University of New York (State University of New York)
• Original Assignee: The Research Foundation for The State University of New York (State University of New York)
• Inventors: Barsimantov, Ohad, McLeod, Kenneth, Schaffer, J. David
• Primary Examiner(s): Evanisko, George R
• Application Number:
  US15/182,087
• Publication Number:
  US 20160361041A1
• Time in Patent Office: 1,323 Days
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• CPC Class Codes: A61B 8/02 Measuring pulse or heart rate
• CPC Class Codes: A61B 8/065 to determine blood output from the heart in general A61B5/029
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/565 involving data transmission via a network
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Cardiac Output (CO) has traditionally been difficult, dangerous, and expensive to obtain. Surrogate measures such as pulse rate and blood pressure have therefore been used to permit an estimate of CO. MEMS technology, evolutionary computation, and time-frequency signal analysis techniques provide a technology to non-invasively estimate CO, based on precordial (chest wall) motions. The technology detects a ventricular contraction time point, and stroke volume, from chest wall motion measurements. As CO is the product of heart rate and stroke volume, these algorithms permit continuous, beat to beat CO assessment. Nontraditional Wavelet analysis can be used to extract features from chest acceleration. A learning tool is preferable to define the packets which best correlate to contraction time and stroke volume.

Claims

1. A method for computing cardiac stroke volume of a subject, comprising:

obtaining parameters of a wavelet packet function and basing the parameters on calibration data from a plurality of members of a population, the wavelet packet function relating information from a transducer representing chest wall movements associated with a respective heartbeat to cardiac stroke volume of the respective heartbeat;

measuring chest wall movements of the subject;

performing a wavelet transform on the measured chest wall movements of the subject to produce a set of wavelet transformed data;

determining, from the wavelet transformed data and the parameters of the wavelet packet function, at least a cardiac stroke volume of the respective heartbeat for the subject; and

one of (a) displaying on a display device, and (b) controlling a therapeutic cardiac device, based on at least one of (i) the determined cardiac stroke volume, and (ii) a value calculated from the determined cardiac stroke volume.

2. A system for computing a cardiac performance, comprising:

a display device configured to present a display;

a transducer configured to sense chest wall movements of a subject and to produce a transducer output representing the sensed chest wall movements;

a memory storing parameters of a wavelet packet function, the stored parameters being based on calibration data from a plurality of members of a population, the wavelet packet function relating sensed chest wall movements associated with a respective heartbeat to cardiac stroke volume of the respective heartbeat

at least one automated processor, configured to;

retrieve, from the memory, stored parameters of the wavelet packet function;

perform a wavelet transform on the sensed chest wall movements; and

calculate at least a cardiac stroke volume estimate of the respective heartbeat from the wavelet transformed sensed chest wall movements and the parameters, using the retrieved parameters for the wavelet packet function; and

one of;

(a) display information selectively dependent on the cardiac stroke volume estimate to the display device, for presentation to a user, and (b) control a therapeutic cardiac medical device selectively dependent on the cardiac stroke volume estimate.

3. The system according to claim 2, wherein the chest wall movements are sensed as vibrations comprising at least frequencies over the range of 5-25 Hz.

4. The system according to claim 2, wherein the transducer comprises an accelerometer.

5. The system according to claim 2, wherein the at least one automated processor is further configured to determine at least one of a heart contraction timing and a heart contraction timing variability, based on the sensed chest wall movements.

6. The system according to claim 2, wherein the at least one automated processor is further configured to determine a heart rate, and to determine a cardiac output, based on at least the determined heart rate and the calculated cardiac stroke volume.

7. The system according to claim 2, wherein the wavelet transform employs at least two different wavelet decomposition paths, each respective wavelet decomposition path employing a respectively different type of wavelet packet, and the wavelet packet function comprises a set of wavelet packet parameters applied to a subset of the different types of wavelet packets of the at least two different wavelet decomposition paths.

8. The system according to claim 7, wherein the wavelet packet function is a genetic algorithm optimized, low computational complexity wavelet packet function, employing parameter coefficients for terms of the at least two different wavelet decomposition paths, adapted to produce the calculated cardiac stroke volume having a high correspondence with a benchmark.

9. The system according to claim 2, wherein the at least one automated processor is further configured to employ at least one of an evolutionary algorithm and a genetic algorithm to define the parameters of an optimal wavelet packet function which optimizes both a correlation of the cardiac stroke volume with a benchmark, and a computational complexity of the wavelet packet function.

10. The system according to claim 2,

wherein the calibration data from the plurality of humans comprises sensed chest wall movements and measured cardiac output for the plurality of humans, and

wherein the wavelet packet function is an optimal wavelet packet function with respect to at least the calibration data,

and the at least one automated processor is further configured to determine:

a heart contraction timing based on the sensed chest wall movements, and

a cardiac output as a function of the determined the stroke volume and heart contraction timing.

11. The system according to claim 2, wherein the wavelet transform comprises a plurality of different mother wavelet types, in a plurality of decomposition paths, in a plurality of different filters at different frequencies.

12. The system according to claim 2, wherein the transducer comprises an accelerometer configured to be placed on the xiphoid process of the sternum.

13. The system according to claim 2, wherein at least one body characteristic of the subject is stored in the memory, and the at least one automated processor is further configured to:

calculate a non-normalized cardiac output value based on the sensed chest wall movements of the subject;

store the non-normalized cardiac output value in the memory;

communicate the non-normalized cardiac output value through a communication port; and

normalize the non-normalized cardiac output value according to the at least one body characteristic of the subject to produce a normalized cardiac output value.

14. The system according to claim 13, wherein the at least one automated processor is further configured to control a communication of the non-normalized cardiac output value over a wireless communication link,

further comprising a smartphone configured to receive the non-normalized cardiac output value through the wireless communication link, and to normalize the non-normalized cardiac output value to produce the cardiac output value normalized for the at least one body characteristic of the subject.

15. The system according to claim 2, wherein the at least one automated processor is further configured to determine at least one of:

a variability of a cardiac stroke volume, and a variability of a cardiac output, based on at least the sensed chest wall movements.

16. The system according to claim 2, wherein the transducer is configured to sense the chest wall movements of the subject during breathing and physical exertion, and the at least one automated processor is further configured to receive as inputs a height and a weight of the subject, either calculate or receive as an input a heart rate of the subject, and to calculate a cardiac output based on at least the height, weight, the calculated cardiac stroke volume, and the heart rate.

17. The system according to claim 2, further comprising a housing configured to be wearable by a human, containing at least:

the transducer configured to sense chest wall movements;

a self-contained power source; and

the at least one automated processor, comprising a microcontroller powered by the self-contained power source,

the at least one automated processor being further configured to process the sensed chest wall movements on a beat-by-beat basis, to store the cardiac stroke volume estimate in the memory, and to communicate the stored cardiac stroke volume estimate through a communication port;

wherein the communicated stored cardiac stroke volume estimate is adapted to be processed by a remote system to calculate a cardiac output.

18. The system according to claim 17, further comprising an acceleration sensor configured to determine an acceleration vector of the housing, the acceleration sensor being distinct from the transducer, the microcontroller being further configured to determine an artifact condition based on the acceleration vector.

19. The system according to claim 2, further comprising an electrocardiogram input configured to provide information for determining a heart contraction timing, the cardiac stroke volume estimate being further calculated dependent on the determined a heart contraction timing.

20. The system according to claim 2, wherein:

the wavelet transform comprises a plurality of different mother wavelet waveforms in a plurality of different decomposition paths, each respective decomposition path having a plurality of respective terms;

the wavelet packet function applies respective parameters to respective terms of the wavelet transform; and

the least one automated processor is further configured to;

perform an iterative genetic algorithm for optimizing the parameters of the wavelet packet function;

convolve the plurality of different mother wavelet waveforms in the plurality of different decomposition paths with at least a portion of the calibration data;

define at least one fitness criterion for the wavelet packet function dependent on the convolution;

optimize the respective parameters, based on at least the iterative genetic algorithm and the defined at least one fitness criterion; and

store the optimized respective parameters in the memory.

21. The system according to claim 20, wherein the at least one automated processor is further configured to together optimize the plurality of respective terms of the wavelet packet function and at least one respective parameter applied to a respective term, based on the at least an iterative genetic algorithm and the at least one fitness criterion.

22. The system according to claim 20, wherein:

the at least one automated processor is further configured to;

determine a correlation R² of the wavelet packet function with a reference at a respective stage of the iterative genetic algorithm; and

calculate the at least one respective parameter applied to each respective term of the wavelet packet function, based on at least the correlation R²,

wherein a precision of the correlation R² is sufficiently limited in order to increase a rate of convergence of the iterative genetic algorithm.

23. The system according to claim 20, wherein the at least one automated processor is further configured to perform the wavelet transform on the sensed chest wall movements to produce a wavelet transform domain representation, and to apply the stored optimized at least one respective parameter to each respective term of the wavelet packet function in the wavelet transform domain representation.

24. The system according to claim 2, wherein the at least a cardiac stroke volume estimate is calculated further dependent on a chest circumference measurement of the subject.

25. A system for computing cardiac performance, comprising:

a memory configured to store parameters of a wavelet packet function, the stored parameters being based on calibration data from a plurality of members of a human population relating chest wall movement data to at least cardiac stroke volume;

a transducer configured to sense chest wall movement data from a respective human;

a wavelet transform processor, configured to produce a wavelet transform of the chest wall movement data of the respective human;

a cardiac stroke volume processor, configured to evaluate the wavelet packet function to at least estimate a cardiac stroke volume of the respective human based on the wavelet transform of the chest wall movement data and the stored parameters of the wavelet packet function; and

one of;

(a) a display device configured to display the estimated cardiac stroke volume; and

(b) a therapeutic cardiac device configured to deliver a therapy to the respective human selectively dependent on the estimated cardiac stroke volume.