  Automatic sleep staging is essential for sleep assessment and disorder
diagnosis. Most existing methods depend on one specific dataset and are limited
to be generalized to other unseen datasets, for which the training data and
testing data are from the same dataset. In this paper, we introduce domain
generalization into automatic sleep staging and propose the task of
generalizable sleep staging which aims to improve the model generalization
ability to unseen datasets. Inspired by existing domain generalization methods,
we adopt the feature alignment idea and propose a framework called SleepDG to
solve it. Considering both of local salient features and sequential features
are important for sleep staging, we propose a Multi-level Feature Alignment
combining epoch-level and sequence-level feature alignment to learn
domain-invariant feature representations. Specifically, we design an
Epoch-level Feature Alignment to align the feature distribution of each single
sleep epoch among different domains, and a Sequence-level Feature Alignment to
minimize the discrepancy of sequential features among different domains.
SleepDG is validated on five public datasets, achieving the state-of-the-art
performance.
