Self-Supervised Learning for Sleep Stage Classification with Predictive and Discriminative Contrastive Coding

The purpose of this paper is to learn efficient representations from raw electroencephalogram (EEG) signals for sleep stage classification via self-supervised learning (SSL). Although supervised methods have gained favorable performance, they heavily rely on manually labeled datasets. Recently, SSL arrives comparable performance with fully supervised methods despite limited labeled data by extracting high-level semantic representations. To alleviate the severe reliance of labels, we propose SleepDPC, a novel sleep stage classification algorithm based on SSL. By incorporating two dedicated predictive and discriminative learning principles, SleepDPC discovers underlying semantics from raw EEG signals in a more efficient manner. We thoroughly evaluate the performance of our proposed method on two publicly available datasets. The experimental results show that our method not only learns meaningful representations but also produces superior performance versus various competing methods despite limited access of labeled data.