Method and apparatus for assessing cardiac and/or mental health

• US 9,706,956 B2
• Filed: 05/02/2016
• Issued: 07/18/2017
• Est. Priority Date: 11/03/2009
• Status: Active Grant

• Current Assignee: VivaQuant LLC
• Original Assignee: VivaQuant LLC
• Inventors: Brockway, Marina, Brockway, Brian
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US15/144,324
• Publication Number:
  US 20160317073A1
• Time in Patent Office: 442 Days
• Field of Search: None
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• 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 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/25 Bioelectric electrodes therefor
• CPC Class Codes: A61B 5/259 using conductive adhesive means, e.g. gels
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• 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 5/363 Detecting tachycardia or bradycardia
• 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 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7235 : Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/7253 : characterised by using transforms
• CPC Class Codes: A61B 7/02 : Stethoscopes acoustic details thereof G10K11/00
• CPC Class Codes: G06F 17/14 : Fourier, Walsh or analogous domain transformations , e.g. Laplace, Hilbert, Karhunen-Loeve, transforms for correlation function computation G06F17/156; spectrum analysers G01R23/16
• CPC Class Codes: G06F 2218/04 : Denoising
• CPC Class Codes: G06F 2218/10 : by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

Aspects of the disclosure are directed to detecting physiological and/or other characteristics of subjects. As may be implemented in accordance with one or more embodiments, a time series of cardiac intervals is computed from a recording of activity of a beating heart of a subject, and the time series is decomposed into subcomponents. An envelope of at least one of the subcomponents is computed, and the presence and/or degree of a depressive or stressed mental state of the subject is detected based upon characteristics of the envelope.

Claims

1. A method comprising:

computing a time series of cardiac intervals from a recording of activity of a beating heart of a subject;

decomposing the time series into subcomponents;

computing an envelope of at least one of the subcomponents; and

determining a degree of mental stress of the subject based upon characteristics of the envelope.

2. The method of claim 1, wherein determining the degree of mental stress based upon one or more characteristics of the envelope includes detecting the presence of one or more of:

a periodic component of a predetermined frequency;

a time averaged mean energy in a first scale that exceeds a time averaged mean energy in a second scale for a specified time; and

increased entropy in one of the subcomponents corresponding to a high frequency band.

3. The method of claim 1, wherein determining the degree of mental stress based upon one or more characteristics of the envelope includes evaluating a degree of randomness of fluctuations in energy of the time series over a predetermined time period.

4. The method of claim 3, wherein evaluating the degree of randomness of the fluctuations in energy includes computing entropy.

5. The method of claim 4, further including computing the metric of entropy using one of sample entropy and approximate entropy.

6. The method of claim 1, wherein computing a time series of inter-beat intervals includes:

detecting a plethora of heart beats in the recording;

computing a raw sequence of inter-beat intervals from consecutive ones of the detected beats;

identifying invalid beats from the detected beats;

creating a cleaned sequence of inter-beat intervals by excluding intervals computed using ones of the beats identified as being invalid; and

resampling said cleaned sequence of inter-beat intervals to create an equispaced time series of inter-beat intervals.

7. The method of claim 6, wherein identifying invalid beats includes identifying invalid beats based on characteristics in the recording selected from the group of:

ventricular arrhythmias;

atrial fibrillation;

characteristics indicating that a signal detected as a beat is not a beat; and

noise beyond a noise threshold.

8. The method of claim 6, wherein identifying invalid beats includes identifying ectopic beats by:

decomposing the recording into subcomponents;

detecting the presence of ventricular depolarizations in the subcomponents;

establishing a window around each detected ventricular depolarization;

computing the energy of high frequency and low-frequency subcomponents in each window;

computing, for each window, the ratio of low frequency energy to high frequency energy; and

detecting the presence of an ectopic beat in a window based upon the computed ratio.

9. The method of claim 8, wherein computing high frequency energy in a window includes:

selecting one or more subcomponents corresponding to high frequency; and

computing the high frequency energy using at least one of the sum of squares of the amplitude and the sum of the absolute values of the amplitude.

10. The method of claim 1, further including measuring the temperature of the subject, and detecting the presence of a CMND state based on the characteristics of the envelope and temperature of the subject.

11. The method of claim 1, further including measuring motor activity of the subject, wherein determining the mental stress includes evaluating characteristics of the activity measurements.

12. The method of claim 1, wherein the time series of cardiac intervals is comprised of at least one of the following:

RR intervals; and

QT intervals.