A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning

Ye Yuan, Guangxu Xun, Fenglong Ma, Qiuling Suo, Hongfei Xue, Kebin Jia, Aidong Zhang

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

12 Citations (Scopus)

Abstract

Epileptic seizure detection using multi-channel scalp electroencephalogram (EEG) signals has gained increasing attention in clinical therapy. Recently, researchers attempt to employ deep learning techniques with channel selection to determine critical channels. However, existing models with such hard selection procedure do not take dynamic constraints into account, since the irrelevant channels vary significantly across different situations. To address these issues, we propose ChannelAtt, an end-to-end multi-view deep learning model with channel-aware attention mechanism, to express multi-channel EEG signals in a high-level space with interpretable meanings. ChannelAtt jointly learns both multi-view representation and its contribution scores. We propose two attention mechanisms to learn the attentional representations of multi-channel EEG signals in time-frequency domain. Experimental results show that the proposed ChannelAtt model outperforms the baselines in detecting epileptic seizures. Analytical results of a case study demonstrate that the learned attentional representations are meaningful.

Original languageEnglish (US)
Title of host publication2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages206-209
Number of pages4
ISBN (Electronic)9781538624050
DOIs
StatePublished - Apr 6 2018
Event2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018 - Las Vegas, United States
Duration: Mar 4 2018Mar 7 2018

Publication series

Name2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
Volume2018-January

Other

Other2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
CountryUnited States
CityLas Vegas
Period3/4/183/7/18

Fingerprint

Electroencephalography
Seizures
Learning
Epilepsy
Scalp
Research Personnel
Deep learning
Therapeutics

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics

Cite this

Yuan, Y., Xun, G., Ma, F., Suo, Q., Xue, H., Jia, K., & Zhang, A. (2018). A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. In 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018 (pp. 206-209). (2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2018.8333405
Yuan, Ye ; Xun, Guangxu ; Ma, Fenglong ; Suo, Qiuling ; Xue, Hongfei ; Jia, Kebin ; Zhang, Aidong. / A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 206-209 (2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018).
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abstract = "Epileptic seizure detection using multi-channel scalp electroencephalogram (EEG) signals has gained increasing attention in clinical therapy. Recently, researchers attempt to employ deep learning techniques with channel selection to determine critical channels. However, existing models with such hard selection procedure do not take dynamic constraints into account, since the irrelevant channels vary significantly across different situations. To address these issues, we propose ChannelAtt, an end-to-end multi-view deep learning model with channel-aware attention mechanism, to express multi-channel EEG signals in a high-level space with interpretable meanings. ChannelAtt jointly learns both multi-view representation and its contribution scores. We propose two attention mechanisms to learn the attentional representations of multi-channel EEG signals in time-frequency domain. Experimental results show that the proposed ChannelAtt model outperforms the baselines in detecting epileptic seizures. Analytical results of a case study demonstrate that the learned attentional representations are meaningful.",
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Yuan, Y, Xun, G, Ma, F, Suo, Q, Xue, H, Jia, K & Zhang, A 2018, A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. in 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 206-209, 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018, Las Vegas, United States, 3/4/18. https://doi.org/10.1109/BHI.2018.8333405

A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. / Yuan, Ye; Xun, Guangxu; Ma, Fenglong; Suo, Qiuling; Xue, Hongfei; Jia, Kebin; Zhang, Aidong.

2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 206-209 (2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018; Vol. 2018-January).

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

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Yuan Y, Xun G, Ma F, Suo Q, Xue H, Jia K et al. A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. In 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 206-209. (2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018). https://doi.org/10.1109/BHI.2018.8333405