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Healthcare"],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>Sleep staging has become a critical task in diagnosing and treating sleep disorders to prevent sleep-related diseases. With growing large-scale sleep databases, significant progress has been made toward automatic sleep staging. However, previous studies face critical problems in sleep studies: the heterogeneity of subjects\u2019 physiological signals, the inability to extract meaningful information from unlabeled data to improve predictive performances, the difficulty in modeling correlations between sleep stages, and the lack of an effective mechanism to quantify predictive uncertainty. In this study, we propose a neural network-based sleep staging model, DREAM, to learn domain generalized representations from physiological signals and model sleep dynamics. DREAM learns sleep-related and subject-invariant representations from diverse subjects\u2019 sleep signals and models sleep dynamics by capturing interactions between sequential signal segments and between sleep stages. We conducted a comprehensive empirical study to demonstrate the superiority of DREAM, including sleep stage prediction experiments, a case study, the usage of unlabeled data, and uncertainty. Notably, the case study validates DREAM\u2019s ability to learn the generalized decision function for new subjects, especially in case there are differences between testing and training subjects. Uncertainty quantification shows that DREAM provides prediction uncertainty, making the model reliable and helping sleep experts in real-world applications.<\/jats:p>","DOI":"10.1145\/3757066","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T15:00:46Z","timestamp":1754319646000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Domain-Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7153-0680","authenticated-orcid":false,"given":"Seungyeon","family":"Lee","sequence":"first","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1733-6155","authenticated-orcid":false,"given":"Thai-Hoang","family":"Pham","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9077-5665","authenticated-orcid":false,"given":"Zhao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Google Research, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4601-0779","authenticated-orcid":false,"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3306253"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2018.2813138"},{"key":"e_1_3_2_4_2","first-page":"1597","volume-title":"International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. 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