EEG-Based Multioutput Classification of Sleep Stage and Apnea Using Deep Learning

Donghyeok Jo, Choel Hui Lee, Hakseung Kim, Hayom Kim, Jung Bin Kim, Dong Joo Kim

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

1 Citation (Scopus)

Abstract

Sleep is closely related to physical and mental health and quality of life, and accurately evaluating sleep quality remains a major research topic in related fields. Conventional methods of sleep quality evaluation involve the use of polysomnography (PSG), which continuously records physiological changes during sleep. With the recorded data, sleep quality, and sleep stage or sleep-related disorders can be diagnosed via manual inspection by trained experts. However, the practice is time-consuming, labor-intensive and yields high inter-and intra-rater variability. To overcome such disadvantages, machine learning or deep learning-based automated techniques have been recently proposed. Most of the existing methods, however, can only classify sleep stage or the presence of apnea, not both. This study proposes a novel method that allows simultaneous classification of both sleep stage and apnea, through multioutput classification using a single deep learning model to comprehensively evaluate sleep quality. PSG recordings from a total of 98 subjects from the ISRUC-Sleep dataset subgroup 1 were analyzed in this study, and the proposed model was trained to classify sleep stage and apnea using three EEG channels. The proposed model showed an accuracy and a micro average f1 score of 0.99 for the classification of apnea, and an accuracy and a micro average f1 score of 0.69 for the classification of the sleep stages. The proposed model provides a new paradigm in sleep deep learning research by enabling comprehensive evaluation of sleep quality, hence could help sleep experts to make clinical decisions.

Original languageEnglish
Title of host publication11th International Winter Conference on Brain-Computer Interface, BCI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665464444
DOIs
Publication statusPublished - 2023
Event11th International Winter Conference on Brain-Computer Interface, BCI 2023 - Virtual, Online, Korea, Republic of
Duration: 2023 Feb 202023 Feb 22

Publication series

NameInternational Winter Conference on Brain-Computer Interface, BCI
Volume2023-February
ISSN (Print)2572-7672

Conference

Conference11th International Winter Conference on Brain-Computer Interface, BCI 2023
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period23/2/2023/2/22

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) Grant funded by the Korean government (Ministry of Science and ICT, MSIT) (No. 2022R1A2C1013205); by Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface).

Publisher Copyright:
© 2023 IEEE.

Keywords

  • EEG
  • Multioutput classification
  • apnea
  • deep learning
  • sleep stage

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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