Method and system for monitoring stress conditions

• US 9,861,308 B2
• Filed: 01/11/2017
• Issued: 01/09/2018
• Est. Priority Date: 06/15/2015
• Status: Expired due to Fees

• Current Assignee: Medibio Ltd.
• Original Assignee: Medibio Ltd.
• Inventors: Flax, Matthew, Wong, Aaron, Player, Michael, Jolly, Todd, Stampfer, Hans
• Primary Examiner(s): VOORHEES, CATHERINE M
• Application Number:
  US15/403,549
• Publication Number:
  US 20170156657A1
• Time in Patent Office: 363 Days
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• 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 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7267 involving training the classification device
• 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: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G16H 10/65 stored on portable record carriers, e.g. on smartcards, RFID tags or CD
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/60 : relating to pathologies
• CPC Class Codes: H04B 1/3827 : Portable transceivers
• CPC Class Codes: H04M 1/72403 : with means for local support of applications that increase the functionality
• CPC Class Codes: H04M 1/72412 : using two-way short-range wireless interfaces
• CPC Class Codes: H04M 2250/12 : including a sensor for measuring a physical value, e.g. temperature or motion telephones including GPS signal receivers H04M2250/10

Abstract

A computer-implemented method of assessing a stress condition of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between stress condition and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the stress condition of the subject, and to generate an indication of stress condition based upon the metric. The indication of stress condition is provided (310) as output.

Claims

1. A computer-implemented method of assessing a mental state of a subject, the method comprising:

receiving, as input, a heartbeat record of the subject, which comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period, a sleep period having a sleep onset time and a sleep conclusion time, and a post-sleep period;

identifying, within the heartbeat record, at least the sleep onset time and the sleep conclusion time;

accessing a knowledge base comprising data obtained via expert evaluation of a training set of subjects and embodying a computational model of a relationship between mental state and heart rate characteristics;

using information in the knowledge base, applying the computational model to a computed at least one metric associated with the mental state of the subject, to generate an indication of mental state based upon the metric, wherein the knowledge base comprises a template heart rate characteristic obtained by averaging scaled and normalised heart rate characteristics of subjects in the training set who have been assessed by an expert assessor as experiencing normal or low levels of stress; and

providing, as output, the indication of mental state.

2. The method of claim 1 wherein the indication of mental state comprises an indication of stress levels experienced by the subject.

3. The method of claim 2 wherein the indication of mental state distinguishes between three levels of stress.

4. The method of claim 1 wherein the knowledge base further comprises a normal centroid value, comprising a measure of an average distance of the heartbeat characteristics of subjects in the training set assessed as normal from the template characteristic.

5. The method of claim 4 wherein the knowledge base further comprises a set of segmentation points, representing variations in distance from the template characteristic, relative to the normal centroid and, defining classification boundaries between different indications of mental state.

6. The method of claim 2 wherein heart rate characteristics of subjects are processed to compute a plurality of associated metrics.

7. The method of claim 6 wherein the metrics comprise:

a mean-awake heart rate;

a ratio between mean-awake and -asleep heart rates;

a slope of heart rate during the first half of the sleep period; and

a slope of heart rate in the second half of the sleep period.

8. The method of claim 6 wherein the knowledge base comprises an array of values of the metrics corresponding with a template characteristic based upon subjects assessed by an expert as having normal or low stress levels.

9. The method of claim 8 wherein the knowledge base further comprises an array of normal centroid values, corresponding with each one of the plurality of metrics, and computed by averaging the magnitudes of the differences between the respective metrics for all subjects assessed by the expert as having normal or low stress levels, and the equivalent template characteristic metrics.

10. The method of claim 9 further comprising performing a comparison between the computed metrics for the received heartbeat record of the subject, and the corresponding normal centroid values, to compute a measure of distance of the subject'"'"'s mental state from a ‘

normal’

mental state, and wherein the knowledge base further comprises an array of segmentation values defining distance measures corresponding with boundaries between mental state classifications.

11. The method of claim 6 wherein the knowledge base comprises an array of (M+1) dimensional vectors, where M is the number of the plurality of metrics employed, in which each vector corresponds with a subject in the training set, and comprises values for each of the computed metrics, and a value of the corresponding expert assessment of the subject.

12. The method of claim 11 wherein the indication of mental state is generated using a k nearest neighbour (k-NN) computational model.

13. The method of claim 12 wherein the knowledge base further comprises an optimised value of the number of nearest-neighbours parameter K.

14. The method of claim 12 wherein the indication of mental state of the subject is generated as the most-frequently occurring mental state associated with the identified K nearest neighbours from the training set.

15. A computer-implemented system for assessing a mental state of a subject, the system comprising:

at least one microprocessor;

at least one non-volatile storage device containing a knowledge base comprising data obtained via expert evaluation of a training set of subjects and embodying a computational model of a relationship between mental state and heart rate characteristics;

at least one computer-readable memory device operatively associated with the microprocessor; and

an input/output interface operatively associated with the microprocessor, wherein the memory device contains computer-executable instruction code which, when executed via the microprocessor, causes the microprocessor to effect a method comprising steps of;

receiving, via the input/output interface, a heartbeat record of the subject, which comprises a sequence of heartbeat data samples obtained over a timespan which includes a pre-sleep period, a sleep period having a sleep onset time and a sleep conclusion time, and a post-sleep period;

identifying, within the heartbeat record, at least the sleep onset time and the sleep conclusion time;

using information in the knowledge base, applying the computational model to a computed at least one metric associated with the mental state of the subject to generate an indication of mental state based upon the metric, wherein the knowledge base comprises a template heart rate characteristic obtained by averaging scaled and normalised heart rate characteristics of subjects in the training set who have been assessed by an expert assessor as experiencing normal or low levels of stress; and

providing, via the input/output interface, the indication of the mental state of the subject.

16. The system of claim 15 wherein the heartbeat record of the subject is obtained via a heart rate monitor device worn by the subject during the timespan including the pre-sleep period, the sleep period and the post-sleep period.

17. The system of claim 16 wherein the heartbeat monitor comprises a communications interface configured for communication with a networkconnected device of the subject.

18. The system of claim 17 wherein the input/output interface comprises a network interface providing access to a wide area network, and the next record is received via the wide area network from the network-connected device of the subject.