A deep learning-based method for sleep stage classification using physiological signal

Guanjie Huang, Chao-hsien Chu, Xiaodan Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A huge number of people suffers from different types of sleep disorders, such as insomnia, narcolepsy, and apnea. A correct classification of their sleep stage is a prerequisite and essential step to effectively diagnose and treat their sleep disorders. Sleep stages are often scored by experts through manually inspecting the patients’ polysomnography which are usually needed to be collected in hospitals. It is very laborious for experts and discommodious for patients to go through the process. Accordingly, current studies focused on automatically identifying the sleep stages and nearly all of them need to use hand-crafted features to achieve a decent performance. However, the extraction and selection of these features are time-consuming and require domain knowledge. In this study, we adopt and present a deep learning approach for automatic sleep stage classification using physiological signal. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) of popular deep learning models are employed to automatically learn features from raw physiological signals and identify the sleep stages. Our experiments shown that the proposed deep learning-based method has better performance than previous work. Hence, it can be a promising tool for patients and doctors to monitor the sleep condition and diagnose the sleep disorder timely.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2018, Proceedings
EditorsHsinchun Chen, Daniel Zeng, Qing Fang, Jiang Wu
PublisherSpringer Verlag
Pages249-260
Number of pages12
ISBN (Print)9783030036485
DOIs
StatePublished - Jan 1 2018
EventInternational Conference on Smart Health, ICSH 2018 - Wuhan, China
Duration: Jul 1 2018Jul 3 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10983 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Smart Health, ICSH 2018
CountryChina
CityWuhan
Period7/1/187/3/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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