ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training. (arXiv:2107.04470v4 [cs.LG] UPDATED)

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 …

arxiv framework iterative lg self-training staging training