Self-Supervised Representation Learning for Sleep Stage Classification with Feature Space Augmentation and Temporal Prediction

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. While supervised methods have demonstrated good performance in sleep stage classification, obtaining large-scale manually labeled datasets remains a challenge. Recently, self-supervised learning has received increasing attention in sleep stage classification. Self-supervised learning uses unlabeled EEG signals to learn representations, reducing the cost of expert labeling. However, the existing self-supervised learning methods often need to manually adjust the data augmentation strategy according to the characteristics of the data, and only learn the representation from the instance level. Therefore, we propose a self-supervised contrastive learning model FSA-TP for sleep stage classification. Firstly, we design a new feature augmentation module for disturbing the temporal features of EEG signals in the feature space to avoid the tedious operation of manually designing data augmentation strategies. Secondly, we propose a temporal prediction module to learn the temporal representation of EEG signals through a cross-view subsequence prediction task. Finally, we improve the quality of negative samples through the negative mixing module. We evaluate the performance of our proposed method on two publicly available sleep datasets. Experimental results show that FSA-TP not only learns meaningful representations but also produces superior performance.