Methods and systems for labeling sleep states

Patent metadata

Field	Value
Publication number	US-11877861-B2
Application number	US-202217690369-A
Country	US
Kind code	B2
Filing date	Mar 9, 2022
Priority date	Sep 6, 2016
Publication date	Jan 23, 2024
Grant date	Jan 23, 2024

Abstract

A system, computer-readable storage medium, and a method capable of, directly or indirectly, estimating sleep states of a user based on sensor data from movement sensors and/or optical sensors.

First claim

What is claimed is:

1. A computer-implemented method for labeling stages of sleep, the computer-implemented method comprising:
executing instructions stored within a non-transitory, machine-readable storage medium that is operatively coupled to one or more processors to cause the one or more processors to perform the following operations:
obtaining, using a wrist-worn device, motion data for a user during a time window in which the user is asleep;
obtaining, using the wrist-worn device, cardiopulmonary data for the user during the time window;
processing the motion data and the cardiopulmonary data with a classifier model to generate one or more labels,
each of the one or more labels generated for a corresponding time period within the time window,
each label indicating the user is awake or in one of a plurality of different stages of sleep during the corresponding time period,
extracting a first feature from the motion data for the user;
extracting a second feature from the cardiopulmonary data or from inter-beat intervals for the user,
wherein the second feature comprises at least one of a variability of a de-trended respiration rate, an inter-percentile spread of a heart rate, and a normalized de-trended heart rate,
and wherein the processing of the motion data and the cardiopulmonary data with the classifier model includes processing the first feature and the second feature to generate the one or more labels;
and displaying, on a graphical user interface, the one or more labels;
where the displaying of the one or more labels includes a graphic report of at least one time period and a label that correspond to the time period.

2. The computer-implemented method of claim 1 , wherein:
the obtaining of the motion data for the user includes obtaining the motion data from a first sensor;
and the obtaining of the cardiopulmonary data for the user includes obtaining the cardiopulmonary data from a second sensor that is different than the first sensor.

3. The computer-implemented method of claim 2 , wherein the first sensor includes an accelerometer and the second sensor includes an optical sensor.

4. The computer-implemented method of claim 3 , wherein the optical sensor includes a photoplethysmogram (PPG) sensor.

5. The computer-implemented method of claim 1 , wherein the first feature includes an amount of time that has lapsed since the motion data last indicated a threshold amount of motion by the user.

6. The computer-implemented method of claim 1 , wherein the second feature includes a difference between a minimum heart rate of the user during a threshold amount of time and a maximum heart rate of the user during the threshold amount of time.

7. The computer-implemented method of claim 1 , wherein a third feature includes:
a variability of an envelope of inter-beat intervals, or a cross-correlation of each pulse shape of one or more pulse shapes in the cardiopulmonary data with a previous pulse shape in the cardiopulmonary data,
wherein the pulse shapes are normalized to a common duration prior to the cross-correlation.

8. The computer-implemented method of claim 1 , wherein the plurality of different stages of sleep include a light sleep stage, a deep sleep stage, and a rapid eye movement (REM) sleep stage.

9. The computer-implemented method of claim 1 , wherein the classifier model includes a random forest classifier or a linear discriminant classifier model.

10. A system for labeling stages of sleep, the system comprising:
a plurality of sensors within a wrist-worn device;
one or more processors; and a non-transitory, machine-readable storage medium operatively coupled to the one or more processors and having stored therein instructions that, when executed, cause the one or more processors to perform operations, the operations including:
obtaining, via one or more sensors of the plurality of sensors, motion data for a user during a time window in which the user is asleep and wearing the wrist-worn device;
obtaining, via one or more sensors of the plurality of sensors, cardiopulmonary data for the user during the time window;
and processing the motion data and the cardiopulmonary data with a classifier model to generate a one or more labels,
each of the one or more labels generated for a corresponding time period within the time window,
each label indicating the user is awake or in one of a plurality of different stages of sleep during the corresponding time period,
extracting a first feature from the motion data for the user;
extracting a second feature from the cardiopulmonary data or from inter-beat intervals for the user,
wherein the second feature comprises at least one of a variability of a de-trended respiration rate, an inter-percentile spread of a heart rate, and a normalized de-trended heart rate,
and wherein the processing of the motion data and the cardiopulmonary data with the classifier model includes processing the first feature and the second feature to generate the one or more labels;
and displaying, on a graphical user interface, the one or more labels;
where the displaying of the one or more labels includes a graphic report of at least one time period and a label that correspond to the time period.

11. The system of claim 10 , wherein:
the obtaining of the motion data for the user includes obtaining, via a first sensor of the plurality of sensors, the motion data;
and the obtaining of the cardiopulmonary data includes obtaining, via a second sensor of the plurality of sensors, the cardiopulmonary data, the second sensor being different than the first sensor.

12. The system of claim 11 , wherein:
the first sensor includes an accelerometer;
and the second sensor includes an optical sensor.

13. The system of claim 10 , wherein the first feature includes an amount of time that has lapsed since the motion data last indicated a threshold amount of motion by the user.

14. The system of claim 10 , wherein the second feature includes a difference between a minimum heart rate of the user during a threshold amount of time and a maximum heart rate of the user during the threshold amount of time.

15. The system of claim 10 , wherein the plurality of different stages of sleep include a light sleep stage, a deep sleep stage, and a rapid eye movement (REM) sleep stage.

16. The system of claim 10 , wherein the classifier model includes a random forest classifier or a linear discriminant classifier model.

17. The system of claim 10 , wherein processing the motion data and the cardiopulmonary data using the classifier model includes:
determining a first confidence score for the user being awake during one or more time periods;
and determining a second confidence score for the user being in a first stage of the plurality of different stages of sleep during the one or more time periods;
determining a third confidence score for the user being a second stage of the plurality of different sleep stages during the one or more time periods;
and labeling the one or more time periods with a label corresponding to a highest of the first confidence score, the second confidence score, and the third confidence score.

18. The computer-implemented method of claim 1 , wherein the classifier model is trained using sleep stage classifications determined by a human for one or more sleep study subjects and based on motion data for the one or more sleep study subjects.

19. The system of claim 10 , wherein the classifier model is trained using sleep stage classifications determined by a human for one or mo

Assignees

• Fitbit Inc

Inventors

• Heneghan Conor Joseph
• Arnold Jacob Anthony
• Beattie Zachary Todd
• Pantelopoulos Alexandros A
• Russell Allison Maya
• Foeckler Philip
• Tucker Adrienne M
• Lopez Delisa
• Lafon Belen
• Ghoreyshi Atiyeh

Classifications

• A61B5/4812 Primary

  Detecting sleep stages or cycles · CPC title

• A61B5/02416

  using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence) · CPC title

• A61B5/02438

  with portable devices, e.g. worn by the patient · CPC title

• A61B5/11

  Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb {(A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721)} · CPC title

• A61B5/1114

  Tracking parts of the body · CPC title