SYSTEM AND METHOD FOR INFRASONIC CARDIAC MONITORING

• US 20160361041A1
• Filed: 06/14/2016
• Published: 12/15/2016
• 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, Schaffer, J. David, McLeod, Kenneth
• Granted Patent: US 10,542,961 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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, comprising:

measuring chest wall movements of a subject;

performing a wavelet transform on the measured movements;

determining, from the wavelet transformed measured movements, at least a cardiac stroke volume, based on calibration data derived from a plurality of subjects; and

outputting information selectively dependent on the determined cardiac stroke volume.

2. The method according to claim 1, wherein the movements are detected as vibrations comprising at least frequencies over the range of 5-25 Hz.

3. The method according to claim 1, wherein the measured movements are detected with an accelerometer.

4. The method according to claim 1, further comprising determining at least one of a heart contraction timing and a heart contraction timing variability, based on the measured movements.

5. The method according to claim 1, further comprising determining a heart rate, and determining a cardiac output based on at least the determined heart rate and the determined stroke volume.

6. The method according to claim 1, wherein the wavelet transform employs at least two different wavelet packets, and associated parameters for each of the respective at least two different wavelet packets for each of at least two decomposition paths.

7. The method according to claim 6, wherein the information selectively dependent on the determined cardiac stroke volume is determined based on an algorithm comprising the wavelet transform, optimized by a genetic algorithm, with optimization criteria comprising low computational complexity and a correspondence of the information with a benchmark.

8. The method according to claim 1, further comprising employing at least one of an evolutionary algorithm and a genetic algorithm to define an optimal wavelet transform, which optimizes correlation with a benchmark and a computational complexity.

9. The method according to claim 1,

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

wherein the wavelet transform is optimized based on at least the measured chest wall movements and measured cardiac output for the plurality of subjects,

further comprising:

determining the stroke volume, and a heart contraction timing, based on the wavelet transformed measured chest wall movements, and

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

10. The method according to claim 1, 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.

11. The method according to claim 1, wherein the chest wall movements are measured by an accelerometer placed on the xiphoid process of the sternum.

12. The method according to claim 1, further comprising:

calculating a non-normalized cardiac output value;

storing the non-normalized cardiac output value;

communicating the non-normalized cardiac output value over a communication link; and

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

13. The method according to claim 12, wherein the non-normalized cardiac output value is communicated over a wireless communication link to a smartphone, and the smartphone normalizes the non-normalized cardiac output value to produce the cardiac output value normalized for the at least one body characteristic of the subject.

14. The method according to claim 1, further comprising determining at least one of:

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

15. The method according to claim 1, further comprising measuring the chest wall movements of the subject during breathing and physical exertion, and receiving as an input a height and a weight of the subject and calculating a cardiac output based on at least the height, weight, determined cardiac stroke volume, and a heart rate.

16. A system for computing cardiac output, comprising:

a transducer configured to sense chest wall movements of a subject;

at least one automated processor, configured to;

perform a wavelet transform on the measured movements based on at least one predefined mother wavelet and at least one set of weights; and

determine, from the wavelet transformed measured chest wall movements, at least a cardiac stroke volume, based on calibration data derived from a plurality of humans;

and

at least one of an output port and a memory, configured to receive information selectively dependent on the determined cardiac stroke volume.

17. The system according to claim 16, further comprising a housing configured to be wearable by a human, containing at least:

the transducer having an output corresponding to chest wall movement;

a self-contained power source; and

the at least one automated processor, comprising a microcontroller powered by the self-contained power source configured to process the transducer output on a beat-by-beat basis, to produce information adapted for estimating the cardiac stroke volume, to store the information in the memory, and to communicate the stored information through the communication port;

wherein the communicated stored information 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 16, further comprising an electrocardiogram input configured to provide information for determining a heart contraction timing.

20. A method for analyzing a signal, comprising:

receiving a series of digitized values representing a physical process;

defining at least one fitness criterion for a wavelet transform on the series of digitized values comprising a convolution of a plurality of different mother wavelet waveforms in a plurality of different decomposition paths, the wavelet transform comprising a plurality of respective terms and at least one respective parameter applied to each respective term;

optimizing the at least one respective parameter applied to each respective term of the wavelet transform, based on at least an iterative genetic algorithm and the defined at least one fitness criterion; and

storing the optimized at least one respective parameter applied to each respective term of the wavelet transform.

21. The method according to claim 20, wherein the plurality of respective terms of the wavelet transform and at least one respective parameter applied to each respective term are together optimized based on the at least an iterative genetic algorithm and the at least one fitness criterion.

22. The method according to claim 20, wherein:

the at least one respective parameter applied to each respective term of the wavelet transform are calculated based on at least a correlation R² of the wavelet transform with a reference at a respective stage of the iterative genetic algorithm, and

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

23. The method according to claim 20, further comprising:

receiving a time-continuous biological signal;

digitizing the biological signal to form the series of digitized values; and

employing the stored optimized at least one respective parameter applied to each respective term of the wavelet transform to process the series of digitized values to determine a biological parameter, substantially without inverting the wavelet transform.

24. The method according to claim 23, wherein the biological signal comprises at least one of an electroencephalogram signal, an electrocardiogram signal, an electromyogram signal, a phonocardiogram signal, a ballistocardiogram signal, an ultrasound signal, an x-ray signal, a magnetic resonance signal, a lung sound signal, and a bowel noise signal.