  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' 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 models sleep
dynamics. DREAM learns sleep related and subject invariant representations from
diverse subjects' 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's
ability to learn 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.
