Self-Supervised Learning for Sleep Stage Classification with Temporal Augmentation and False Negative Suppression

Self-supervised learning has been gaining attention in the field of sleep stage classification. It learns representations with unlabeled electroencephalography (EEG) signals, which alleviates the cost of labeling for specialists. However, most self-supervised approaches assume only the two augmented views from the same EEG sample is a positive pair, which suffers from the false negative problem. Therefore, we propose a new model named Temporal Augmentation and False Negative Suppression (TA-FNS) to solve the problem. Specifically, it first generates two augmented views for each EEG sample. Then the temporal augmentation module is proposed to learn temporal features during sleep from augmented views. Based on temporal features, intra-view and inter-view sample similarity matrices are calculated. Finally, the false negative suppression module identifies and eliminates potential false negatives according to the consistency between intra-view and inter-view similarity matrices. TA-FNS not only achieves state-of-the-art performances on Sleep-EDF and ISRUC datasets, but also learns semantic representation from EEG of different sleep stages, which demonstrates the effectiveness of it in mitigating the false negative problem.