US 2020/0205709 A1Published application

Mental state indicator

AssigneeMedibio Limited

InventorsYashar BEHZADI, Nathan KOWAHL, Matthew WESCOTT, Nick HUGHES, Sangyeop LEE

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Abstract

A monitoring system for generating a mental state indicator for use in identifying a mental state of a biological subject, including one or more electronic processing devices that obtain subject data indicative of at least a heart rate measured for the biological subject during at least part of a sleep episode, analyze the subject data to determine at least one sleep segment; analyze the subject data to determine at least one metric for the at least one sleep segment, and apply the at least one metric to a computational model to determine a mental state indicator indicative of a mental state, the computational model embodying a relationship between different mental states and one or more metrics, and being obtained by applying machine learning to reference metrics derived from heart rates measured for one or more reference subjects during at least part of a reference sleep period.

Claims36

Dependent claims

A monitoring system for generating a mental state indicator for use in identifying a mental state of a biological subject, the monitoring system including one or more electronic processing devices that:

a) obtain subject data indicative of at least a heart rate measured for the biological subject during at least part of a sleep episode;
b) analyze the subject data to determine at least one sleep segment selected from the group including:
i) n minutes preceding sleep onset;
ii) n minutes following sleep onset;
iii) the sleep episode;
iv) a first half of the sleep episode;
v) a second half of the sleep episode;
vi) n minutes prior to waking;
c) analyze the subject data to determine at least one metric for the at least one sleep segment, the at least one metric being selected from the metric group including:
i) a heart rate statistic metric selected from a heart rate statistic group including:
(1) a mean;
(2) a median;
(3) an average;
(4) a variance:
(5) a skew;
(6) a kurtosis;
(7) a percentile;
(8) a cumulative distribution function;
ii) a heart rate spectral power metric indicative of a spectral power in at least one frequency band selected from a frequency band group including:
(1) an ultra low frequency less than about 0.003 Hz;
(2) a very low frequency between about 0.003 Hz and about 0.04 Hz;
(3) a low frequency between about 0.04 Hz and about 0.15 Hz;
(4) a high-frequency between about 0.15 Hz and about 0.4 Hz;
iii) a heart rate variability metric selected from a heart rate variability group including:
(1) a multi-scale entropy;
(2) a standard deviation of average pulse intervals;
and,
(3) square root of the mean of the squares of differences between adjacent pulse intervals;
and,
d) apply the at least one metric to at least one computational model to determine an mental state indicator indicative of a mental state, the at least one computational model embodying a relationship between different mental states and one or more metrics, the at least one computational model being obtained by applying machine learning to reference metrics derived from heart rates measured for one or more reference subjects during at least part of a reference sleep period.

27 more elements

A method for generating an mental state indicator for use in identifying a mental state of a biological subject, the method including in one or more electronic processing devices:

a) obtaining subject data indicative of at least a heart rate measured for the biological subject during at least part of a sleep episode;
b) analyzing the subject data to determine at least one sleep segment selected from the group including:
i) i minutes preceding sleep onset;
ii) n minutes following sleep onset;
iii) the sleep period;
iv) a first half of the sleep period;
v) a second half of the sleep period;
vi) n minutes prior to waking;
c) analyzing the subject data to determine at least one metric for the at least one sleep segment, the at least one metric being selected from the metric group including:
i) a heart rate statistic metric selected from a heart rate statistic group including:
(1) a mean;
(2) a median;
(3) an average;
(4) a variance;
(5) a skew;
(6) a kurtosis;
(7) a percentile;
(8) a cumulative distribution function;
ii) a heart rate spectral power metric indicative of a spectral power in at least one frequency band selected from a frequency band group including:
(1) an ultra low frequency less than 0.003 Hz;
(2) a very low frequency between 0.003 Hz and 0.04 Hz;
(3) a low frequency between 0.04 Hz and 0.15 Hz;
(4) a high-frequency between about 0.15 Hz and about 0.4 Hz;
iii) a heart rate variability metric selected from a heart rate variability group including:
(1) a multi-scale entropy;
(2) a standard deviation of average pulse intervals;
and,
(3) square root of the mean of the squares of differences between adjacent pulse intervals;
and,
d) applying the at least one metric to at least one computational model to determine an mental state indicator indicative of a mental state, the at least one computational model embodying a relationship between different mental states and one or more metrics, the at least one computational model being obtained by applying machine learning to reference metrics derived from heart rates measured for one or more reference subjects during at least part of a reference sleep period.

27 more elements

A system for use in calculating at least one computational model, the at least one computational model being used for generating an mental state indicator for use in identifying a mental state of a biological subject, the system including one or more electronic processing devices that:

a) for each of a plurality of reference subjects:
i) obtain reference subject data indicative of:
(1) at least a heart rate measured the reference subject during at least part of a reference sleep episode;
and,
(2) a diagnosed mental state of the reference subject;
ii) analyze the reference subject data to determine at least one sleep segment selected from the group including:
(1) n minutes preceding sleep onset;
(2) n minutes following sleep onset;
(3) the sleep episode;
(4) a first half of the sleep episode;
(5) a second half of the sleep episode;
(6) n minutes prior to waking;
iii) analyze the reference subject data to determine at least one reference metric for the at least one reference sleep segment, the at least one metric being selected from the metric group including:
(1) a heart rate statistic metric selected from a heart rate statistic group including:
(a) a mean;
(b) a median;
(c) an average;
(d) a variance;
(e) a skew;
(f) a kurtosis;
(g) a percentile;
(h) a cumulative distribution function;
(2) a heart rate spectral power metric indicative of a spectral power in at least one frequency band selected from a frequency band group including:
(a) an ultra low frequency less than about 0.003 Hz;
(b) a very low frequency between about 0.003 Hz and about 0.04 Hz;
(c) a low frequency between about 0.04 Hz and about 0.15 Hz;
(d) a high-frequency between about 0.15 Hz and about 0.4 Hz;
(3) a heart rate variability metric selected from a heart rate variability group including:
(a) a multi-scale entropy;
(b) a standard deviation of average pulse intervals;
and,
(c) square root of the mean of the squares of differences between adjacent pulse intervals;
and,
b) use the at least one reference metric and diagnosed mental state for a number of reference subjects to train at least one computational model, the at least one computational model embodying a relationship between different mental states and the at least one reference metric.

31 more elements

A method for use in calculating at least one computational model, the at least one computational model being used for generating an mental state indicator for use in identifying a mental state of a biological subject, the method including, in one or more electronic processing devices:

a) for each of a plurality of reference subjects:
i) obtaining reference subject data indicative of:
(1) at least a heart rate measured the reference subject during at least part of a reference sleep episode;
and,
(2) a diagnosed mental state of the reference subject;
ii) analyzing the reference subject data to determine at least one sleep segment selected from the group including:
(1) n minutes preceding sleep onset;
(2) n minutes following sleep onset;
(3) the sleep episode;
(4) a first half of the sleep episode;
(5) a second half of the sleep episode;
(6) n minutes prior to waking;
iii) analyzing the reference subject data to determine at least one reference metric for the at least one reference sleep segment, the at least one metric being selected from the metric group including:
(1) a heart rate statistic metric selected from a heart rate statistic group including:
(a) a mean;
(b) a median;
(c) an average;
(d) a variance;
(e) a skew;
(f) a kurtosis;
(g) a percentile;
(h) a cumulative distribution function;
(2) a heart rate spectral power metric indicative of a spectral power in at least one frequency band selected from a frequency band group including:
(a) an ultra low frequency less than about 0.003 Hz;
(b) a very low frequency between about 0.003 Hz and about 0.04 Hz;
(c) a low frequency between about 0.04 Hz and about 0.15 Hz;
(d) a high-frequency between about 0.15 Hz and about 0.4 Hz;
(3) a heart rate variability metric selected from a heart rate variability group including:
(a) a multi-scale entropy;
(b) a standard deviation of average pulse intervals;
and,
(c) square root of the mean of the squares of differences between adjacent pulse intervals;
and,
b) using the at least one reference metric and diagnosed mental state for a number of reference subjects to train at least one computational model, the at least one computational model embodying a relationship between different mental states and the at least one reference metric.

31 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/621,644Filed 11 Jun 2018
Priority: 12 Jun 2017
Published: 2 Jul 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification13 codes

A61B 5/16
Measuring for diagnostic purposes ; Identification of persons: devices for psychotechnics ; Testing reaction times ; Devices for evaluating the…
A61B 5/02405
Measuring pulse rate or heart rate: determining heart rate variability
A61B 5/318
Modalities, i.e. specific diagnostic methods: heart-related electrical modalities, e.g. electrocardiography [ECG]
A61B 5/372
Electroencephalography [EEG]: analysis of electroencephalograms