Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging
CoRR(2023)
摘要
Sleep staging has become a critical task in diagnosing and treating sleep
disorders to prevent sleep related diseases. With rapidly growing large scale
public sleep databases and advances in machine learning, significant progress
has been made toward automatic sleep staging. However, previous studies face
some critical problems in sleep studies; the heterogeneity of subjects'
physiological signals, the inability to extract meaningful information from
unlabeled sleep signal 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 automatic sleep staging model, named 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 signal segments and models sleep dynamics by capturing
interactions between sequential signal segments and between sleep stages. In
the experiments, we demonstrate that DREAM outperforms the existing sleep
staging methods on three datasets. The case study demonstrates that our model
can learn the generalized decision function resulting in good prediction
performances for the new subjects, especially in case there are differences
between testing and training subjects. The usage of unlabeled data shows the
benefit of leveraging unlabeled EEG data. Further, uncertainty quantification
demonstrates that DREAM provides prediction uncertainty, making the model
reliable and helping sleep experts in real world applications.
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