Open AccessMachine Learning‐Enabled Noncontact Sleep Structure Prediction
Author(s) -
Zhai Qian,
Tang Tingyu,
Lu Xiaoling,
Zhou Xiaoxi,
Li Chunguang,
Yi Jingang,
Liu Tao
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202100227
Subject(s) - sleep (system call) , polysomnography , computer science , convolutional neural network , radar , artificial intelligence , random forest , sleep stages , deep learning , artificial neural network , machine learning , autoregressive model , medicine , electroencephalography , telecommunications , statistics , mathematics , psychiatry , operating system
Automated, effective and efficient sleep‐stage monitoring and structure analysis is an essential enabling procedure for healthcare automation. Sleep diagnosis by polysomnography is a golden standard but expensive procedure involving huge effort from patients. There remain challenges for smart devices to precisely identify sleep stage and minimize intrusive effect on sleep progression. Herein, a novel noncontact sleep structure prediction system (NSSPS) using a single radar sensor is presented to analyze sleep structure without any tethered unit. The NSSPS is realized through training a convolutional recurrent neural network and neural conditional random fields using reflected radio frequency (RF) waves acquired by radar antennas. By capturing implicit temporal information in RF signals and transitions of sleep progression, high accuracy of sleep‐stage prediction is achieved and characteristics of sleep structure are extracted. The performance of the NSSPS is validated by transfer learning between radar signals with different frequency bands and crossvalidation among different subjects. Moreover, the NSSPS is demonstrated to estimate overnight parameters that are critical for sleep diagnosis. Benefiting from its low cost, convenient setup, and accurate prediction capability of sleep‐stage identification, the NSSPS can be widely deployed in “smart” homes and exploited to conduct daily sleep structure analysis.