System and method of extraction of the heart valve signals

• US 10,165,985 B2
• Filed: 01/03/2017
• Issued: 01/01/2019
• Est. Priority Date: 01/04/2016
• Status: Active Grant

• Current Assignee: AventuSoft, LLC
• Original Assignee: AventuSoft, LLC
• Inventors: Kale, Kaustubh, Giraldo, Luis Gonzalo Sanchez
• Primary Examiner(s): Getzow, Scott M.
• Application Number:
  US15/397,040
• Publication Number:
  US 20170188869A1
• Time in Patent Office: 728 Days
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/06 Arrangements of multiple sensors of different types
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0008 Temperature signals
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0044 for the heart
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/02444 Details of sensor A61B5/02427 takes precedence
• CPC Class Codes: A61B 5/0536 Impedance imaging, e.g. by tomography
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1107 Measuring contraction of parts of the body, e.g. organ, muscle apparatus for measuring work or force in general G01L
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/332 Portable devices specially adapted therefor
• CPC Class Codes: A61B 5/335 using integrated circuit memory devices
• CPC Class Codes: A61B 5/346 : Analysis of electrocardiograms
• CPC Class Codes: A61B 5/352 : Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/361 : Detecting fibrillation
• CPC Class Codes: A61B 5/364 : Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/366 : Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/6801 : specially adapted to be attached to or worn on the body surface
• 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 5/6833 : Adhesive patches
• CPC Class Codes: A61B 5/7203 : for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7214 : using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
• CPC Class Codes: A61B 5/7225 : Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/725 : using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7257 : using Fourier transforms
• 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 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 7/04 : Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/047 : Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/048 : Activation functions
• CPC Class Codes: G06N 3/088 : Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

A system for monitoring and diagnosing heart conditions includes a sensor array with accelerometers, an electrocardiogram sensor, and a system to capture and process the composite heart signals. The system performs source separation to extract information contained in cardio pulmonic vibrations. The system can use machine learning, auditory scene analysis, spare coding, determined Models, Principal Component Analysis (PCA), Independent Component Analysis ICA, Singular Value Decomposition (SVD), Bin-wise Clustering and Permutation posterior probability alignment, Undetermined Models, Sparseness condition, Dictionary learning, Convolutive models, K-SVD Matching Pursuit, Non-negative matrix factorization and Deep Belief Networks (Restricted Boltzmann Machine) approaches to the source separation problem. Other embodiments are disclosed.

Claims

1. A system for measuring cardiac time intervals, comprising:

a non-invasive sensor unit for capturing electrical signals and composite vibration objects; and

a processor for separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks; and

wherein the processor is further configured for identifying the individual valve events from the plurality of individual heart vibration events with respect to an electrocardiogram signal for presenting the individual valve events superimposed with a synchronized electrocardiogram signal.

2. The system for measuring cardiac time intervals of claim 1, wherein the non-invasive sensor unit includes a first sensor configured for placement at or near a pulmonary auscultation location and a second sensor configured for placement at or near an aortic auscultation location.

3. The system for measuring cardiac time intervals of claim 1, wherein the non-invasive sensor unit includes a sensor configured for placement at or near a tricuspid auscultation location and a sensor configured for placement at or near a mitral auscultation location.

4. The system for measuring cardiac time intervals of claim 1, wherein the non-invasive sensor unit includes a first sensor configured for placement at a pulmonary auscultation location, a second sensor configured for placement at an aortic auscultation location, and at least a third sensor configured for placement at a tricuspid auscultation location or at a mitral auscultation locations or both.

5. The system for measuring cardiac time intervals of claim 1, further comprising a sensor for performing an electrocardiogram and wherein the non-invasive sensor unit comprises at least one sensor for sensing a heart valve opening.

6. The system of claim 1, wherein the permutation alignment is a posterior based permutation alignment.

7. The system of claim 1, wherein the deep belief networks uses stacked modules of restricted Boltzmann machines to create a context frame having a concatenation of feature vectors from frames that are consecutive in time.

8. The system for measuring cardiac time intervals of claim 1, wherein the processor is configured to separate the plurality of individual heart vibration events from the composite vibration objects from multichannel signals by decomposing the multichannel signals into sparse activation patterns and clustering the sparse activation patterns.

9. The system for measuring cardiac time intervals of claim 1, wherein the system comprises a wearable device in the form of a patch, band, necklace, belt, or bandage configured to communicate with a wireless node and further configured to capture an electrocardiogram signal synchronized with one or more accelerometer sensors of the non-invasive sensor unit.

10. The system for measuring cardiac time intervals of claim 1, wherein the non-invasive sensor unit comprises at least one sensor for sensing vibrations corresponding to a heart valve opening.

11. The system for measuring cardiac time intervals of claim 1, wherein a number of vibration sensors in the non-invasive sensor unit is less than a number of separated vibration sources coming from one or more of a mitral valve, a tricuspid valve, a aortic valve, and/or a pulmonary valve.

12. The system for measuring cardiac time intervals of claim 1, wherein the processor separates a plurality of individual heart vibration events from composite vibration objects from multichannel signals using at least one source separation technique used for extracting the valve heart signals for application in a non-linear time variant system, among Determined Models, Principal Component Analysis (PCA), Independent Component Analysis ICA, Singular Value Decomposition (SVD), Bin-wise Clustering and Permutation posterior probability Alignment, Undetermined Models, Sparseness condition, Dictionary learning, Convolutive models, or K-SVD Matching Pursuit.

13. The system for measuring cardiac time intervals of claim 1, wherein a marking of individual valve events comprises at least the marking of one or more among a Mitral valve opening (MO), Aortic valve opening (AO), Tricuspid valve opening (TO), or Pulmonary valve opening (PO).

14. A sensor array device, comprising:

at least one accelerometer;

a wearable device configured to capture an electrocardiogram signal synchronized with composite vibration objects from the at least one accelerometer and further configured to communicate with a wireless node;

wherein the at least one accelerometer is configured for capturing the composite vibration objects;

an electrode for sensing the electrocardiogram signal; and

a processor for separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks.

15. The sensor array device of claim 14, wherein the at least one accelerometer are configurable for measuring a lower frequency range vibration signal and a higher frequency range vibration signal.

16. The sensor array device of claim 14, wherein the processor is operatively coupled to the at least one accelerometer, the processor further being configured for:

identifying the plurality of individual heart vibration events from the composite vibration objects;

transmitting the composite vibration objects or the plurality of individual heart vibration events to a remote device; and

marking and presenting individual valve events from the plurality of individual heart vibration events with respect to the electrocardiogram signal.

17. The sensor array device of claim 14, wherein the at least one accelerometer comprises a first sensor configured for placement at or near a pulmonary auscultation location, a second sensor configured for placement at or near an aortic auscultation location, and at least a third sensor configured for placement at or near a tricuspid auscultation location or at or near a mitral auscultation location or both.

18. The sensor array device of claim 14, wherein the sensor array device is portable and captures synchronized sensor data to a memory and wherein the processor is configurable in the process of separating the plurality of individual heart vibration events from the composite vibration objects into separate vibration sources and further identifying the individual heart vibration events among at least one of a mitral valve opening, a tricuspid valve opening, an aortic valve opening, or a pulmonary valve opening.

19. The sensor array device of claim 14, further comprising a wireless connection for transmission of sensor data or processed sensor data to a remote computing device or a cloud computing device.

20. A system of measuring cardiac time intervals, comprising:

a memory having computer instructions; and

one or more processors operatively coupled to the memory, the execution of the computer instructions causing the one or more processors to perform operations comprising;

capturing composite vibration objects using a non-invasive sensor unit;

separating, by the one or more processors, a plurality of individual heart vibration events from the composite vibration objects using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks;

identifying, by the one or more processors, the individual valve events from the plurality of individual heart vibration events with respect to an electrocardiogram signal, wherein the individual valve events comprises at least one among a mitral valve opening, a tricuspid valve opening, an aortic valve opening, or a pulmonary valve opening; and

presenting the individual valve events superimposed with a synchronized electrocardiogram signal.