Stress detection based on sympathovagal balance

• US 10,231,673 B2
• Filed: 06/15/2016
• Issued: 03/19/2019
• 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
• Primary Examiner(s): Evanisko, George
• Application Number:
  US15/183,481
• Publication Number:
  US 20170071551A1
• Time in Patent Office: 1,007 Days
• 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 comprising:

measuring, by a sensor of a computing device, a heart-rate variability (HRV) characteristic of a user over a period of time;

by the computing device, generating a histogram from the set of measurement data;

by the computing device, accessing a user-specific baseline sympathovagal balance (SVB) value of the user;

by the computing device, dividing the histogram into a first portion and a second portion based on the user-specific baseline SVB value;

by the computing device, determining a first number of autonomic nervous system (ANS) activity markers in the first portion of the histogram;

by the computing device, determining a second number of ANS activity markers in the second portion of the histogram;

by the computing device, determining a ratio of the first number to the second number;

by the computing device, estimating a stress level of the user based on the ratio; and

displaying, on a display of the computing device, a stress-related notification based on the estimated stress level of the user, whereby the displayed stress-related notification is for monitoring and treating the user'"'"'s stress.

2. The method of claim 1, wherein the user-specific baseline SVB value comprises a baseline HRV value in a histogram of a baseline set of measurement data for the HRV characteristic, wherein the baseline value corresponds to a position in the histogram of the baseline set of measurements where the number of ANS activity markers below the baseline HRV value substantially equal the number of ANS activity markers above the baseline HRV value.

3. The method of claim 2, wherein the HRV characteristic comprises a temporal variability in intervals between heart beats of the user over the period of time.

4. The method of claim 3, wherein the ANS activity markers comprise the number of times a temporal interval between heart beats or a range of temporal intervals between heart beats occurred in the set of measurement data.

5. The method of claim 4, further comprising:

accessing sensor data comprising one or more of;

accelerometer data;

or

blood-flow data for the user; and

estimating the stress level of the user based on the ratio and on the accessed sensor data.

6. The method of claim 1, wherein:

the sensor of the computing device comprises a sensor of a wearable computing device.

7. 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.

8. The method of claim 7, 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.

9. The method of claim 8, wherein estimating the stress level of the user further comprises estimating the stress level of the user based on a ratio of a number of the SNS activity markers to the number of the PSNS activity markers.

10. The method of claim 9, wherein the user-specific SVB value corresponds to a position in a histogram of a baseline set of measurement data for the HRV characteristic such that the number of the SNS activity markers substantially equals the number of the PSNS activity markers.

11. The method of claim 1, further comprising determining, based on a first probabilistic weight for at least one of the ANS activity markers, a contribution of that ANS activity marker to the first number.

12. The method of claim 11, further comprising determining, based on the first probabilistic weight, that the at least one of the ANS activity markers identifies sympathetic nervous system (SNS) activity in an amount proportional to the first probabilistic weight.

13. The method of claim 11, further comprising determining, based on a second probabilistic weight for the at least one of the ANS activity markers, a contribution of that ANS activity marker to the second number.

14. The method of claim 13, further comprising determining, based on the second probabilistic weight, that the at least one of the ANS activity markers identifies parasympathetic nervous system (PSNS) activity in an amount proportional to the second probabilistic weight.

15. The method of claim 1, wherein the user-specific baseline SVB value comprises a range of values in the histogram.

16. The method of claim 1, wherein the notification comprises an identification of the estimated stress level of the user.

17. The method of claim 16, wherein the identification of the estimated stress level of the user is presented in connection with a sequence of estimated stress levels of the user over a period of time.

18. The method of claim 1, wherein:

the method further comprises comparing, by the computing device, the estimated stress level of the user to a threshold stress level; and

the notification comprises an alert that the estimated stress level of the user exceeds the threshold stress level.

19. One or more computer-readable non-transitory storage media embodying software for that is operable when executed to:

measure, by a sensor of a computing device, a heart-rate variability (HRV) characteristic of a user over a period of time;

generate a histogram from the set of measurement data;

access a user-specific baseline sympathovagal balance (SVB) value of the user;

divide the histogram into a first portion and a second portion based on the user-specific baseline SVB value;

determine a first number of autonomic nervous system (ANS) activity markers in the first portion of the histogram;

determine a second number of ANS activity markers in the second portion of the histogram;

determine a ratio of the first number to the second number;

estimate a stress level of the user based on the ratio; and

display on a display of the computing device a stress-related notification based on the estimated stress level of the user, whereby the displayed stress-related notification is for monitoring and treating the user'"'"'s stress.

20. A system comprising:

one or more processors; and

a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

measure, by a sensor of a computing device, a heart-rate variability (HRV) characteristic of a user over a period of time;

generate a histogram from the set of measurement data;

access a user-specific baseline sympathovagal balance (SVB) value of the user;

divide the histogram into a first portion and a second portion based on the user-specific baseline SVB value;

determine a first number of autonomic nervous system (ANS) activity markers in the first portion of the histogram;

determine a second number of ANS activity markers in the second portion of the histogram;

determine a ratio of the first number to the second number;

estimate a stress level of the user based on the ratio; and

display on a display of the computing device a stress-related notification based on the estimated stress level of the user, whereby the displayed stress-related notification is for monitoring and treating the user'"'"'s stress.