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

doi:10.1109/ICTC49870.2020.9289335

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 proceeding › Conference contribution

10 Citations (Scopus)

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 language	English
Title of host publication	ICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publication	Data, Network, and AI in the Age of Untact
Publisher	IEEE Computer Society
Pages	394-398
Number of pages	5
ISBN (Electronic)	9781728167589
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289335
Publication status	Published - 2020 Oct 21
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 2020 Oct 21 → 2020 Oct 23

Publication series

Name	International Conference on ICT Convergence
Volume	2020-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	20/10/21 → 20/10/23

Keywords

classification
deep learning
electrocardiogram
electroencephalography
ensemble
Sleep stages

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC49870.2020.9289335

Cite this

Wang, I. N., Lee, C. H., Kim, H. J., Kim, H., & Kim, D. J. (2020). An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 394-398). Article 9289335 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289335

An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG. / Wang, In Nea; Lee, Choel Hui; Kim, Hyun Ji et al.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 394-398 9289335 (International Conference on ICT Convergence; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, IN, Lee, CH, Kim, HJ, Kim, H & Kim, DJ 2020, An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289335, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 394-398, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 20/10/21. https://doi.org/10.1109/ICTC49870.2020.9289335

Wang IN, Lee CH, Kim HJ, Kim H, Kim DJ. An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society. 2020. p. 394-398. 9289335. (International Conference on ICT Convergence). doi: 10.1109/ICTC49870.2020.9289335

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

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. ",

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author = "Wang, \{In Nea\} and Lee, \{Choel Hui\} and Kim, \{Hyun Ji\} and Hakseung Kim and Kim, \{Dong Joo\}",

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AB - 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.

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

Abstract

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Keywords

ASJC Scopus subject areas

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