Method and system for assessing mental state
First Claim
1. A computer-implemented method of assessing a mental state of a subject, the method comprising:
- a) 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;
b) identifying, within the heartbeat record, at least the sleep onset time and the sleep conclusion time;
c) computing metrics of the subject from the heartbeat record, the metrics including;
i) a mean awake heart rate calculated as an average heart rate during the pre-sleep and post-sleep periods;
ii) a ratio of a mean awake heart rate and a mean asleep heart rate;
iii) a first slope metric indicative of a change over time of the subject'"'"'s heart rate during a first half of the sleep period; and
,iv) a second slope metric indicative of a change over time of the subject'"'"'s heart rate during a second half of the sleep period;
d) accessing a knowledge base which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and said metrics, the computational model comprising data structures representing classification trees obtained by applying a decision tree learning algorithm over said metrics computed by processing heartbeat records of a training set of subjects and the classification trees including;
i) a first classification tree data structure that classifies metrics computed from the heartbeat record of the subject into ‘
normal’
or ‘
not normal’
; and
ii) a second classification tree data structure that classifies the metrics computed from the heartbeat record of the subject into ‘
depressed’ and
‘
not depressed;
e) applying the metrics of the subject to the computational model to generate an indication of mental state by;
i) classifying the subject as ‘
normal’
or ‘
not normal’
by executing the first classification tree; and
ii) in the event that the subject is classified as ‘
not normal’
, classifying the subject as ‘
depressed’
or ‘
not depressed’
by executing the second classification tree; and
f) providing, as output, the indication of mental state.
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Abstract
A computer-implemented method of assessing a mental state 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 mental state 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 mental state of the subject, and to generate an indication of mental state based upon the metric. The indication of mental state is provided (310) as output.
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Citations
16 Claims
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1. A computer-implemented method of assessing a mental state of a subject, the method comprising:
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a) 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; b) identifying, within the heartbeat record, at least the sleep onset time and the sleep conclusion time; c) computing metrics of the subject from the heartbeat record, the metrics including; i) a mean awake heart rate calculated as an average heart rate during the pre-sleep and post-sleep periods; ii) a ratio of a mean awake heart rate and a mean asleep heart rate; iii) a first slope metric indicative of a change over time of the subject'"'"'s heart rate during a first half of the sleep period; and
,iv) a second slope metric indicative of a change over time of the subject'"'"'s heart rate during a second half of the sleep period; d) accessing a knowledge base which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and said metrics, the computational model comprising data structures representing classification trees obtained by applying a decision tree learning algorithm over said metrics computed by processing heartbeat records of a training set of subjects and the classification trees including; i) a first classification tree data structure that classifies metrics computed from the heartbeat record of the subject into ‘
normal’
or ‘
not normal’
; andii) a second classification tree data structure that classifies the metrics computed from the heartbeat record of the subject into ‘
depressed’ and
‘
not depressed;e) applying the metrics of the subject to the computational model to generate an indication of mental state by; i) classifying the subject as ‘
normal’
or ‘
not normal’
by executing the first classification tree; andii) in the event that the subject is classified as ‘
not normal’
, classifying the subject as ‘
depressed’
or ‘
not depressed’
by executing the second classification tree; andf) providing, as output, the indication of mental state. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented system for assessing a mental state of a subject, the system comprising:
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g) at least one microprocessor; h) at least one non-volatile storage device containing a knowledge base which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and metrics computed from a heartbeat record, the computational model comprising data structures representing classification trees obtained by applying a decision tree learning algorithm over said metrics computed by processing heartbeat records of a training set of subjects and the classification trees including; i) a first classification tree data structure that classifies metrics computed from the heartbeat record of the subject into ‘
normal’
or ‘
not normal’
; andii) a second classification tree data structure that classifies the metrics computed from the heartbeat record of the subject into ‘
depressed’ and
‘
not depressed;i) at least one computer-readable memory device operatively associated with the microprocessor; and j) an input/output interface operatively associated with the microprocessor, k) 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; i) 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; ii) identifying, within the heartbeat record, at least the sleep onset time and the sleep conclusion time; iii) computing metrics of the subject from the heartbeat record, the metrics including; (1) a mean awake heart rate calculated as an average heart rate during the pre-sleep and post-sleep periods; (2) a ratio of a mean awake heart rate and a mean asleep heart rate; (3) a first slope metric indicative of a change over time of the subject'"'"'s heart rate during a first half of the sleep period; (4) a second slope metric indicative of a change over time of the subject'"'"'s heart rate during a second half of the sleep period; iv) applying the metrics of the subject to the computational model to generate an indication of mental state by; (1) classifying the subject as ‘
normal’
or ‘
not normal’
by executing the first classification tree; and(2) in the event that the subject is classified as ‘
not normal’
, classifying the subject as ‘
depressed’
or ‘
not depressed’
by executing the second classification tree; andv) providing, via the input/output interface, the indication of the mental state of the subject. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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Specification