Contrastive Learning for Sleep Staging based on Inter Subject
  Correlation
Tongxu Zhang Bei Wang
In recent years, multitudes of researches have applied deep learning to
automatic sleep stage classification. Whereas actually, these works have paid
less attention to the issue of cross-subject in sleep staging. At the same
time, emerging neuroscience theories on inter-subject correlations can provide
new insights for cross-subject analysis. This paper presents the MViTime model
that have been used in sleep staging study. And we implement the inter-subject
correlation theory through contrastive learning, providing a feasible solution
to address the cross-subject problem in sleep stage classification. Finally,
experimental results and conclusions are presented, demonstrating that the
developed method has achieved state-of-the-art performance on sleep staging.
The results of the ablation experiment also demonstrate the effectiveness of
the cross-subject approach based on contrastive learning.