METHOD AND SYSTEM FOR MEASURING BEAT PARAMETERS

• US 20160100803A1
• Filed: 10/08/2015
• Published: 04/14/2016
• Est. Priority Date: 10/08/2014
• Status: Active Grant

• Current Assignee: MAD Apparel Incorporated
• Original Assignee: MAD Apparel Incorporated
• Inventors: Korzinov, Lev, Gordhandas, Ankit, Wiebe, Christopher
• Granted Patent: US 10,524,734 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/04 Arrangements of multiple sensors of the same type
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/366 Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G16H 40/63 for local operation

Abstract

A method for communicating beat parameters to a user includes: providing an electrode module comprising a first and a second set of electrodes, associated with a first and a second sensor channel, respectively; receiving a first and a second dataset based on a first and a second set of bioelectrical signals detected from the first and the second sensor channel, respectively; receiving a supplemental dataset based on supplemental bioelectrical signals detected from a supplemental sensor module; generating a noise-mitigated power spectrum upon: generating a combined dataset based upon combining the first and second datasets, calculating 1) a heart power spectrum based on the combined data set, and 2) a supplemental power spectrum based on the supplemental dataset, and generating a noise-mitigated power spectrum based on processing the heart power spectrum with the supplemental power spectrum; and rendering information derived from a beat parameter analysis to the user.

Claims

1. A method for communicating beat parameters to a user at a user interface of an electronic device associated with the user, the method comprising:

providing, by way of a garment worn by the user, an electrode module coupled to a body region located inferior to a L1 lumbar vertebrae region of the user, wherein the electrode module comprises a first electrode pair, associated with a first sensor channel, and a second electrode pair, associated with a second sensor channel, and wherein a first vector between electrodes of the first electrode pair and a second vector between electrodes of the second electrode pair are substantially crossed;

receiving, at a processing subsystem in communication with the electrode module,

     1) a first dataset based on a first set of bioelectrical signals detected from the first sensor channel, and

     2) a second dataset based on a second set of bioelectrical signals detected from the second sensor channel, wherein the first dataset and the second dataset comprise a local noise component and a heart signal component;

receiving, at the processing subsystem, an electromyography (EMG) dataset based on a set of EMG signals detected from an EMG sensor module of the garment interfacing with the user;

generating a noise-mitigated power spectrum upon;

generating a combined dataset based upon non-linearly combining the first and second datasets, wherein the combined dataset comprises a dampened local noise component and an accentuated heart signal component;

calculating

     1) a heart power spectrum based on the combined data set, and

     2) a combined EMG power spectrum based on combining a plurality of EMG power spectra calculated from the EMG dataset;

generating a noise-mitigated power spectrum based on processing the heart power spectrum with the combined EMG power spectrum;

generating a beat parameter analysis based upon the noise-mitigated power spectrum; and

rendering information derived from the beat parameter analysis to the user at the user interface.

2. The method of claim 1, wherein the first vector and the second vector lie substantially within a transverse plane through the user.

3. The method of claim 1 wherein generating the noise-mitigated power spectrum comprises combining a heart signal fundamental frequency component of the heart signal component with a heart signal harmonic frequency component of heart signal component.

4. The method of claim 3, wherein combining the plurality of EMG power spectra comprises combining an EMG fundamental frequency component of an EMG signal from the set of EMG signals with an EMG harmonic frequency component of the EMG signal.

5. The method of claim 1 further comprising identifying a frequency parameter with highest power in the noise-mitigated power spectrum, and wherein generating the beat parameter analysis comprises generating the beat parameter analysis based upon the frequency parameter with the highest power.

6. The method of claim 1, wherein rendering information derived from the beat parameter analysis comprises sending a notification indicative of a cardiovascular health condition of the user.

7. The method of claim 1, wherein generating the noise-mitigated power spectrum comprises generating the noise-mitigated power spectrum based on a non-linear combination of the heart signal power spectrum and the combined EMG power spectrum.

8. The method of claim 7, wherein the non-linear combination of the heart signal power spectrum and the combined EMG power spectrum is based on dividing the heart signal power spectrum by the combined EMG power spectrum.

9. The method of claim 1, wherein calculating the heart signal power spectrum comprises calculating the heart signal power spectrum based on a sample of the combined data set, and wherein the sample comprises a heart signal component segment corresponding to a time interval from a set of time intervals.

10. The method of claim 9 wherein generating the beat parameter analysis comprises:

determining a set of heart rate components, wherein the set of heart rate components is associated with the set of time intervals in a one-to-one manner; and

calculating a heart rate variability based on the set of heart rate components.

11. A method for communicating beat parameters to a user at a user interface of an electronic device associated with the user, the method comprising:

providing, by way of a garment worn by the user, an electrode module comprising a first set of electrodes, associated with a first sensor channel, and a second set of electrodes, associated with a second sensor channel;

receiving, at a processing subsystem in communication with the electrode module,

     1) a first dataset based on a first set of bioelectrical signals detected from the first sensor channel, and

     2) a second dataset based on a second set of bioelectrical signals detected from the second sensor channel, wherein the first dataset and the second dataset comprise a local noise component and a heart signal component;

generating, at the processing subsystem, a combined dataset based upon non-linearly combining the first and second datasets, wherein the combined dataset comprises a dampened local noise component and an accentuated heart signal component;

generating a sample of the combined dataset based on the combined dataset, wherein the sample of the combined dataset includes a heart signal component segment corresponding to a time interval associated with a time interval length;

generating, an analysis of a beat parameter based upon the sample of the combined dataset; and

rendering information derived from the beat parameter analysis to the user at the user interface.

12. The method of claim 11, further comprising updating the time interval length based on the beat parameter analysis.

13. The method of claim 11, wherein generating the combined dataset comprises combining a heart signal fundamental frequency component of the heart signal component with a heart signal harmonic frequency component of the heart signal component.

14. The method of claim 11 wherein the time interval is from a set of time intervals, and wherein generating the beat parameter analysis comprises:

determining a set of heart rate components, wherein the set of heart rate components is associated with the set of time intervals in a one-to-one manner; and

calculating a heart rate variability based on the set of heart rate components.

15. The method of claim 11, wherein generating the combined dataset comprises:

identifying a noise removed subspace substantially orthogonal to the heart signal component based on a correlation between the first dataset and the second dataset; and

extracting the noise removed subspace from the combined dataset.

16. The method of claim 11, wherein the non-linear combination of the first dataset and the second dataset is based on multiplying the first dataset and the second dataset, and dividing by the difference of the first dataset and the second dataset.

17. The method of claim 11, wherein generating the beat parameter analysis comprises predicting a confidence parameter indicating an accuracy level of the beat parameter, based on running a predictive model trained on features predictive of the beat parameter accuracy level, and wherein rendering information derived from the beat parameter analysis comprises:

comparing the confidence parameter to a threshold condition; and

in response to the confidence parameter satisfying the threshold condition, rendering information derived from the beat parameter analysis.

18. The method of claim 11, wherein generating the beat parameter analysis comprises:

identifying a signature corresponding to a QRS complex of the heart signal component; and

determining a QRS complex characteristic based on the signature.

19. A system for communicating beat parameters to a user at a user interface of an electronic device associated with user, the system comprising:

a garment configured to be worn by the user at a first body region located inferior to a L1 lumbar vertebrae region of the user;

a heart signal sensor module coupled to the garment and comprising;

a first electrode pair and a second electrode pair oriented such that a first vector between electrodes of the first electrode pair and a second vector between electrodes of the second electrode pair are substantially crossed,

a first sensor channel associated with the first electrode pair, and configured to generate a first dataset based on a first set of detected bioelectrical signals, and

a second sensor channel associated with the second electrode pair, and configured to generate a second dataset based on a second set of detected bioelectrical signals, wherein the first dataset and the second dataset comprise a local noise component and a heart signal component;

an electromyography (EMG) sensor module coupled to the garment, and configured to generate an EMG dataset based on detected EMG signals; and

a control module comprising;

a communication subsystem in communication with the heart signal sensor module and the EMG sensor module; and

a processing subsystem in communication with the communication subsystem, and configured to;

generate a combined dataset based upon non-linearly combining the first and second datasets, wherein the combined dataset comprises a dampened local noise component and an accentuated heart signal component,

calculate

     1) a heart power spectrum based on the combined data set, and

     2) a combined EMG power spectrum based on combining a plurality of EMG power spectrums calculated from the EMG dataset,

generate a noise-mitigated power spectrum based on processing the heart power spectrum with the combined EMG power spectrum,

generate an analysis of a beat parameter based upon the noise-mitigated power spectrum, and

generate information to be rendered to the user at the user interface, wherein the information is derived from the beat parameter analysis.

20. The system of claim 19, wherein the first vector and the second vector lie substantially within a transverse plane through the user, and wherein the EMG sensor module interfaces with the user at a second body region located substantially proximal to at least one of a group of muscles comprising:

a gluteus maximus muscle, a vastus lateralus muscle, and a triceps surea muscle.

21. The system of claim 19, wherein the processing subsystem is further configured to identify a frequency parameter with highest power in the noise-mitigated power spectrum, and wherein the processing subsystem configured to generate the beat parameter analysis comprises the processing subsystem configured to generate the beat parameter analysis based upon the frequency parameter with the highest power.