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Sleep staging has become a critical task in diagnosing and treating sleepdisorders to prevent sleep related diseases. With growing large scale sleepdatabases, significant progress has been made toward automatic sleep staging.However, previous studies face critical problems in sleep studies; theheterogeneity of subjects' physiological signals, the inability to extractmeaningful information from unlabeled data to improve predictive performances,the difficulty in modeling correlations between sleep stages, and the lack ofan effective mechanism to quantify predictive uncertainty. In this study, wepropose a neural network based sleep staging model, DREAM, to learn domaingeneralized representations from physiological signals and models sleepdynamics. DREAM learns sleep related and subject invariant representations fromdiverse subjects' sleep signals and models sleep dynamics by capturinginteractions between sequential signal segments and between sleep stages. Weconducted a comprehensive empirical study to demonstrate the superiority ofDREAM, including sleep stage prediction experiments, a case study, the usage ofunlabeled data, and uncertainty. Notably, the case study validates DREAM'sability to learn generalized decision function for new subjects, especially incase there are differences between testing and training subjects. Uncertaintyquantification shows that DREAM provides prediction uncertainty, making themodel reliable and helping sleep experts in real world applications.

Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging