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AI Biotech/Diagnostics: Cardio
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Patent US10638965
Issued 2020-05-05
Method And System For Monitoring Stress Conditions
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.
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- 1. A computer-implemented method of assessing a mental state of a subject, the method comprising:
receiving a heartbeat record indicative of a heart rate of the subject over a time span including 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; analysing the heartbeat record using the identified sleep onset time and the sleep conclusion time to determine at least one heart rate metric; applying the at least one heart rate metric to a computational model of a relationship between mental state and heart rate characteristics to generate an indication of mental state based upon the at least one heart rate metric, the computation model being obtained by applying machine learning to at least one heart rate metric derived from heart rates measured for subjects in a training set who have been assessed by an expert assessor; and providing, as output, the indication of mental state; wherein the computational model forms part of a knowledge base including an array of (M+1) dimensional vectors, where M is the number of the plurality of heart rate metrics employed, in which each vector corresponds with a subject in the training set, and comprises values for each of the at least one heart rate metric, and a value of the corresponding expert assessment of the subject.
- 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 computational model of a relationship between mental state and heart rate characteristics, the computation model being obtained by applying machine learning to at least one heart rate metric derived from heart rates measured for subjects in a training set who have been assessed by an expert assessor; at least one computer-readable memory device operatively associated with the microprocessor; and
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 a heartbeat record indicative of a heart rate 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; analysing the heartbeat record using the identified sleep onset time and the sleep conclusion time to determine at least one heart rate metric; applying the at least one heart rate metric to the computational model to generate an indication of mental state based upon the at least one heart rate metric; and providing, via the input/output interface, the indication of the mental state of the subject; wherein the computational model forms part of a knowledge base including an array of (M+1) dimensional vectors, where M is the number of the plurality of heart rate metrics employed, in which each vector corresponds with a subject in the training set, and comprises values for each of the at least one heart rate metric, and a value of the corresponding expert assessment of the subject.
