An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG

In Nea Wang, Choel Hui Lee, Hyun Ji Kim, Hakseung Kim, Dong Joo Kim

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

Abstract

Classification of sleep stages is important for diagnosis and treatment of sleep disorder. Manual classification performed by sleep experts is burdensome and time-consuming. This study proposes a novel model for sleep stage classification. EEG and EOG signals of 153 healthy subjects was used. The proposed model ensembles two EEGNet-BiLSTM models which learn EEG and EOG respectively. Compared to the existing models, the two models yielded approximately 82% accuracy and 0.78 k-value, whereas the proposed ensemble model showed 90% accuracy and 0.80 k-value. The proposed ensemble model is superior in terms of accuracy and consistency compared to the conventional models.

Original languageEnglish
Title of host publicationICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publicationData, Network, and AI in the Age of Untact
PublisherIEEE Computer Society
Pages394-398
Number of pages5
ISBN (Electronic)9781728167589
DOIs
Publication statusPublished - 2020 Oct 21
Event11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of
Duration: 2020 Oct 212020 Oct 23

Publication series

NameInternational Conference on ICT Convergence
Volume2020-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference11th International Conference on Information and Communication Technology Convergence, ICTC 2020
CountryKorea, Republic of
CityJeju Island
Period20/10/2120/10/23

Keywords

  • classification
  • deep learning
  • electrocardiogram
  • electroencephalography
  • ensemble
  • Sleep stages

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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