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Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>Sleep stage classification is crucial for sleep state monitoring and health interventions. In accordance with the standards prescribed by the American Academy of Sleep Medicine, a sleep episode follows a specific structure comprising five distinctive sleep stages that collectively form a sleep cycle. Typically, this cycle repeats about five times, providing an insightful portrayal of the subject\u2019s physiological attributes. The progress of deep learning and advanced domain generalization methods allows automatic and even adaptive sleep stage classification. However, applying models trained with visible subject data to invisible subject data remains challenging due to significant individual differences among subjects. Motivated by the periodic category-complete structure of sleep stage classification, we propose a Structure Incentive Domain Adversarial learning\u00a0(SIDA) method that combines the sleep stage classification method with domain generalization to enable cross-subject sleep stage classification. SIDA includes individual domain discriminators for each sleep stage category to decouple subject dependence differences among different categories and fine-grained learning of domain-invariant features. Furthermore, SIDA directly connects the label classifier and domain discriminators to promote the training process. Experiments on three benchmark sleep stage classification datasets demonstrate that the proposed SIDA method outperforms other state-of-the-art sleep stage classification and domain generalization methods and achieves the best cross-subject sleep stage classification results.<\/jats:p>","DOI":"10.1145\/3625238","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T12:06:58Z","timestamp":1695298018000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8896-0070","authenticated-orcid":false,"given":"Shuo","family":"Ma","sequence":"first","affiliation":[{"name":"Beijing Key Lab. of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6582-1745","authenticated-orcid":false,"given":"Yingwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shangdong Academy of Intelligent Computing Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8407-0780","authenticated-orcid":false,"given":"Yiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shangdong Academy of Intelligent Computing Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0866-5081","authenticated-orcid":false,"given":"Tao","family":"Xie","sequence":"additional","affiliation":[{"name":"NIO, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3921-2683","authenticated-orcid":false,"given":"Shuchao","family":"Song","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8523-1419","authenticated-orcid":false,"given":"Ziyu","family":"Jia","sequence":"additional","affiliation":[{"name":"Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"issue":"6","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/TIM.2018.2799059","article-title":"Ensemble SVM method for automatic sleep stage classification","volume":"67","author":"Alickovic Emina","year":"2018","unstructured":"Emina Alickovic and Abdulhamit Subasi. 2018. 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