Learning sleep stages from radio signals

A sleep stage and signal technology, applied in informatics, medical informatics, medical science, etc., can solve the problems of lack of promotion and low accuracy, and achieve the effect of improving insensitivity

Pending Publication Date: 2019-11-29
MASSACHUSETTS INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Accuracy is relatively low (about 64%), and the model does not generalize beyond the single environment where the measurements were collected

Method used

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  • Learning sleep stages from radio signals
  • Learning sleep stages from radio signals
  • Learning sleep stages from radio signals

Examples

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Embodiment Construction

[0034] refer to figure 1 , the sleep stage processing system 100 monitors a sleeping subject 101 and infers the subject's sleep stage as a function of time. A collection of various predefined classes are available when classifying sleep stages. For example, the stages may include a predetermined enumeration that includes, but is not limited to, the four major categories of "awake," "light sleep," "deep sleep," and "rapid eye movement (REM)." System 100 includes a signal acquisition system 110 that processes an input signal 102 representing activity of a subject, such as sensing breathing or other movement of the subject. As discussed further below, the signal acquisition system 110 can use various contact or non-contact methods by acquiring a signal representing the subject's breathing, heart rate, or both (e.g., representing the subject's breathing and heart rate caused by the subject's breathing and heart rate). motion of the subject) to sense the activity of the subject. ...

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Abstract

A method for tracking a sleep stage of a subject takes as input a sequence of observations sensed over an observation time period. The sequence of observation values is processed to yield a corresponding sequence of encoded observations using a first artificial neural network (ANN) and the sequence of encoded observation values is processed to yield a sequence of sleep stage indicators using a second artificial network. Each observation may correspond to an interval of the observation period (e.g., at least 30 seconds). The first ANN may be configured to reduce information representing a source of the sequence of observations in the encoded observations.

Description

[0001] Cross References to Related Applications [0002] This application claims U.S. Provisional Application 62 / 476,815, filed March 26, 2017, entitled "Learning Sleep Stages from Radio Signals" and U.S. Provisional Application 62, filed June 12, 2017, entitled "Learning Sleep Stages from Radio Signals" / 518,053 priority, both of which are hereby incorporated by reference. This application is also related to US Patent Publication 2017 / 0042432, entitled "Vital Signs Monitoring Via Radio Reflections," and US Patent 9,753,131, entitled "Motion Tracking Via Body Radio Reflections," both of which are also incorporated herein by reference. Background technique [0003] The present invention relates to inferring the sleep stage of a subject via radio signals. [0004] Sleep plays a vital role in an individual's health and well-being. Sleep progresses in cycles involving multiple sleep stages: wakefulness, light sleep, deep sleep, and REM (rapid eye movement). Different stages are...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20
CPCG16H50/20A61B5/4812G06N5/046G06N3/02A61B5/24
Inventor 赵明敏岳世超迪纳·卡塔比T·S·贾科拉
Owner MASSACHUSETTS INST OF TECH
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