  Sleep staging is of great importance in the diagnosis and treatment of sleep
disorders. Recently, numerous data-driven deep learning models have been
proposed for automatic sleep staging. They mainly train the model on a large
public labeled sleep dataset and test it on a smaller one with subjects of
interest. However, they usually assume that the train and test data are drawn
from the same distribution, which may not hold in real-world scenarios.
Unsupervised domain adaption (UDA) has been recently developed to handle this
domain shift problem. However, previous UDA methods applied for sleep staging
have two main limitations. First, they rely on a totally shared model for the
domain alignment, which may lose the domain-specific information during feature
extraction. Second, they only align the source and target distributions
globally without considering the class information in the target domain, which
hinders the classification performance of the model while testing. In this
work, we propose a novel adversarial learning framework called ADAST to tackle
the domain shift problem in the unlabeled target domain. First, we develop an
unshared attention mechanism to preserve the domain-specific features in both
domains. Second, we design an iterative self-training strategy to improve the
classification performance on the target domain via target domain pseudo
labels. We also propose dual distinct classifiers to increase the robustness
and quality of the pseudo labels. The experimental results on six cross-domain
scenarios validate the efficacy of our proposed framework and its advantage
over state-of-the-art UDA methods. The source code is available at
https://github.com/emadeldeen24/ADAST.
