Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155623
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dc.contributor.authorEldele, Emadeldeenen_US
dc.contributor.authorChen, Zhenghuaen_US
dc.contributor.authorLiu, Chengyuen_US
dc.contributor.authorWu, Minen_US
dc.contributor.authorKwoh, Chee Keongen_US
dc.contributor.authorLi, Xiaolien_US
dc.contributor.authorGuan, Cuntaien_US
dc.date.accessioned2022-03-14T02:33:26Z-
dc.date.available2022-03-14T02:33:26Z-
dc.date.issued2021-
dc.identifier.citationEldele, E., Chen, Z., Liu, C., Wu, M., Kwoh, C. K., Li, X. & Guan, C. (2021). An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Transactions On Neural Systems and Rehabilitation Engineering, 29, 809-818. https://dx.doi.org/10.1109/TNSRE.2021.3076234en_US
dc.identifier.issn1534-4320en_US
dc.identifier.urihttps://hdl.handle.net/10356/155623-
dc.description.abstractAutomatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration (AFR). The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features. The second module is the temporal context encoder (TCE) that leverages a multi-head attention mechanism to capture the temporal dependencies among the extracted features. Particularly, the multi-head attention deploys causal convolutions to model the temporal relations in the input features. We evaluate the performance of our proposed AttnSleep model using three public datasets. The results show that our AttnSleep outperforms state-of-the-art techniques in terms of different evaluation metrics. Our source codes, experimental data, and supplementary materials are available at https://github.com/emadeldeen24/AttnSleep.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineeringen_US
dc.relation.uri10.21979/N9/EUHGHSen_US
dc.relation.uri10.21979/N9/EAMYFOen_US
dc.relation.uri10.21979/N9/MA1AVGen_US
dc.rights© 2021 The Author(s). Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en_US
dc.subjectComputer and Information Scienceen_US
dc.subjectEngineeringen_US
dc.titleAn attention-based deep learning approach for sleep stage classification with single-channel EEGen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TNSRE.2021.3076234-
dc.description.versionPublished versionen_US
dc.identifier.pmid33909566-
dc.identifier.scopus2-s2.0-85105092101-
dc.identifier.volume29en_US
dc.identifier.spage809en_US
dc.identifier.epage818en_US
dc.subject.keywordsSleep stage classification, sleep-edf, SHHSen_US
dc.description.acknowledgementThe work of Emadeldeen Eldele was supported by A*STAR SINGA Scholarship.en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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