Expert Knowledge Inspired Contrastive Learning for Sleep Staging

Although supervised deep learning methods achieve favorable performance in automatic sleep staging, they are limited in clinical situations due to the heavy reliance on massive labeled multi-channel polysomnogram (PSG) recordings. Accordingly, to alleviate the reliance on labeled PSG, we present SleepECL, a self-supervised learning framework based on electroencephalogram (EEG) signals taking advantage of contrastive learning, which learns efficient representations by contrasting semantically consistent and inconsistent samples (a.k.a. positive samples and negative samples). Specifically, SleepECL conducts contrastive learning upon local representations (i.e., intra-epoch EEG decoding) as well as contextual representations (i.e., interepoch dependency) and incorporates sleep expert knowledge to discover more accurate positive samples in contrastive learning, leading to more effective representations. Experimental results on two publicly available datasets demonstrate that our SleepECL outperforms state-of-the-art self-supervised methods. Moreover, the pre-trained model achieves acceptable performance using only a few label single-channel EEG recordings, which contributes to a more convenient application of automatic sleep staging in clinical situations.