Contrastive Learning for Sleep Staging based on Inter Subject Correlation
This addresses the challenge of cross-subject variability in sleep staging, which is important for improving automated sleep analysis in medical applications, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the cross-subject problem in automatic sleep stage classification by applying contrastive learning based on inter-subject correlation theory, achieving state-of-the-art performance as demonstrated in experiments.
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.