Stress Detection Based on Sympathovagal Balance

• US 20170071551A1
• Filed: 06/15/2016
• Published: 03/16/2017
• Est. Priority Date: 07/16/2015
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Jain, Jawahar, Young, James, Wortham, Cody, Sadi, Sajid, Mistry, Pranav
• Granted Patent: US 10,231,673 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2560/0209 adapted for power saving
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0013 Medical image data A61B1/00011, A61B6/56, A61B8/56 take precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/4884 inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6833 Adhesive patches
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7225 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 5/7257 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 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• 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 5/7405 : using sound
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 : characterised by tactile indication, e.g. vibration or electrical stimulation
• 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: G16H 40/60 : for the operation of medical equipment or devices
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation

Abstract

A method for determining a stress level of a user based on a sympathovagal balance (SVB) value calculated based on a set of measurement data may include determining a heart-rate variability (HRV) characteristic as a ratio involving a number of autonomic nervous system (ANS) activity markers within a first portion of the set of measurement data and the number of ANS activity markers within a second portion of the set of measurement data, and then determining the stress level of the user based on the HRV characteristic. The first and second portions of the set of measurement data may be selected based on a user-specific baseline SVB value that divides a histogram representation of the set of measurement data into the first and second portions.

Claims

1. A method for determining a stress level of a user based on a sympathovagal balance (SVB) value calculated from a set of measurement data, comprising:

determining a heart-rate variability (HRV) characteristic as a ratio involving a number of autonomic nervous system (ANS) activity markers within a first portion of the set of measurement data and the number of ANS activity markers within a second portion of the set of measurement data, and

determining the stress level of the user based on the HRV characteristic,

wherein the first and second portions of the set of measurement data are selected based on a user-specific baseline SVB value that divides a histogram representation of the set of measurement data into the first and second portions.

2. The method of claim 1, further comprising, prior to determining the HRV characteristic:

determining a baseline SVB value based at least on user-specific baseline data;

determining the first portion of the set of measurement data that is on a first side of the SVB, and

processing the first portion of the set of measurement data to determine the number of ANS activity markers within the first portion.

3. The method of claim 2, wherein the second portion of the set of measurement data comprises the set of measurement data less the first portion of the set of measurement data.

4. The method of claim 3, further comprising:

processing the second portion of the set of measurement data to determine the number of ANS activity markers within the second portion.

5. The method of claim 1, further comprising:

receiving, from a sensor of an electronic device of the user, the set of measurement data, and

generating, based on the set of measurement data, a histogram of the ANS activity markers,

wherein the histogram of the ANS activity markers is divided into the first and second portions such that the number of ANS activity markers in the first portion substantially equal the number of ANS activity markers in the second portion.

6. The method of claim 5, wherein the user-specific baseline SVB value corresponds to a position on the histogram where the number of ANS activity markers in the first portion substantially equal the number of ANS activity markers in the second portion.

7. The method of claim 5, wherein the electronic device comprises at least a wearable electronic device, and

wherein the histogram of the ANS activity markers is determined by the wearable electronic device.

8. The method of claim 5, wherein the user-specific baseline SVB value is determined by the electronic device.

9. The method of claim 5, wherein the stress level of the user is determined by comparing the number of number of ANS activity markers in the first portion with the number of ANS activity markers in the second portion.

10. The method of claim 1, wherein the ANS activity markers comprise one or more sympathetic nervous system (SNS) activity markers and one or more parasympathetic nervous system (PSNS) activity markers.

11. The method of claim 10, wherein the ANS activity markers within the first portion of the set of measurement data comprise the one or more SNS activity markers, and

wherein the ANS activity markers within the second portion of the set of measurement data comprise the one or more PSNS activity markers.

12. The method of claim 11, wherein the HRV characteristic is determined based on a ratio involving a number of the SNS activity markers to the a number of the PSNS activity markers.

13. The method of claim 12, wherein the HRV characteristic is determined based on the ratio of the number of the SNS activity markers to the number of the PSNS activity markers being approximately 50:

50.

14. The method of claim 1, wherein the ANS activity markers contribute to the first and second portions based on a first probabilistic weight corresponding to the first portion and a second probabilistic weight corresponding to the second portion.

15. The method of claim 14, wherein each of the ANS activity markers contributes to sympathetic nervous system (SNS) activity based on the first probabilistic weight and to parasympathetic nervous system (PSNS) activity based on the second probabilistic weight.

16. The method of claim 15, wherein the user-specific baseline SVB value corresponds to a range of SVB values, and

wherein the stress level of the user corresponds to a range of stress-level values.

17. The method of claim 1, further comprising:

generating, based on the set of measurement data, a histogram of the ANS activity markers,

wherein the histogram of the ANS activity markers is divided into the first and second portions, and

wherein an amount of weighted left or right shift of a histogram profile is determined based on a weighted scaled area computation of the histogram.

18. The method of claim 17, wherein the amount of weighted left or right shift of the histogram profile is determined based on an envelope characterization of the histogram.

19. One or more computer-readable non-transitory storage media embodying software for determining a stress level of a user based on a sympathovagal balance (SVB) value calculated from a set of measurement data, that is operable when executed to:

determine a heart-rate variability (HRV) characteristic as a ratio involving a number of autonomic nervous system (ANS) activity markers within a first portion of the set of measurement data and the number of ANS activity markers within a second portion of the set of measurement data, and

determine the stress level of the user based on the HRV characteristic,

wherein the first and second portions of the set of measurement data are selected based on a user-specific baseline SVB value that divides a histogram representation of the set of measurement data into the first and second portions.

20. A system comprising:

one or more processors; and

a non-transitory memory coupled to the processors comprising instructions executable by the processors for determining a stress level of a user based on a sympathovagal balance (SVB) value calculated from a set of measurement data, the processors operable when executing the instructions to;

determine a heart-rate variability (HRV) characteristic as a ratio involving a number of autonomic nervous system (ANS) activity markers within a first portion of the set of measurement data and the number of ANS activity markers within a second portion of the set of measurement data, and

determine the stress level of the user based on the HRV characteristic,

wherein the first and second portions of the set of measurement data are selected based on a user-specific baseline SVB value that divides a histogram representation of the set of measurement data into the first and second portions.