A Single-Channel Sleep Staging Method Based on Self-Supervised Learning

Accurate sleep staging plays a pivotal role in the diagnosis and treatment of sleep-related disorders, yet manual annotation remains a costly task. This study introduces a self-supervised learning approach for sleep staging, requiring minimal labeled data. It is further supported by a novel convolutional neural network framework, utilizing single-channel EEG data for accurate sleep stage identification. Extensive experiments conducted on three datasets—DOD-O, sleep_edf, and challenge2018—demonstrate the efficacy of the proposed method. The results indicate that the self-supervised learning method based on single-channel EEG signals notably enhances the network's feature learning capabilities, endowing the model with greater generalizability and robustness. The performance in sleep staging tasks surpasses fully supervised methods, achieving accuracies of 71.873%, 84.02%, and 79.08% on the three datasets, respectively.