Dual-Branch Self-Supervised Contrastive Pre-Training Framework for Sleep Stage Classification

Accurate sleep staging is vital for evaluating sleep quality and diagnosing sleep disorders. Yet most automated sleep staging methods rely on large datasets labeled by experts. However, clinical annotation is both time-consuming and subjective, making it difficult to obtain sufficient high-quality data for automated sleep staging research. To address this bottleneck, we propose a few-shot, dual-branch contrastive pre-training framework for single-channel electroencephalogram (EEG)-based sleep staging. The framework first conducts fully self-supervised pre-training on unlabeled data, then performs fine-tuning that requires only a small set of labeled samples. We developed and evaluated our solution with the public Sleep-EDF-v2 EEG dataset, achieving state-of-the-art results despite using limited labeled data. Specifically, with only 1% labeled data, our method delivers an accuracy of 76.10% and Macro F1-score of 61.34%, comparable to supervised models trained on 100% labeled data. We further validated our approach on the ISRUC-1 and ISRUC-3 datasets, where similar robust results were consistently observed. The ability to effectively develop sleep classification models using minimal labeled data demonstrates the potential value of our framework across diverse clinical settings.