The classification of sleep stages is a pivotal aspect of diagnosing sleep
disorders and evaluating sleep quality. However, the conventional manual
scoring process, conducted by clinicians, is time-consuming and prone to human
bias. Recent advancements in deep learning have substantially propelled the
automation of sleep stage classification. Nevertheless, challenges persist,
including the need for large datasets with labels and the inherent biases in
human-generated annotations. This paper introduces NeuroNet, a self-supervised
learning (SSL) framework designed to effectively harness unlabeled
single-channel sleep electroencephalogram (EEG) signals by integrating
contrastive learning tasks and masked prediction tasks. NeuroNet demonstrates
superior performance over existing SSL methodologies through extensive
experimentation conducted across three polysomnography (PSG) datasets.
Additionally, this study proposes a Mamba-based temporal context module to
capture the relationships among diverse EEG epochs. Combining NeuroNet with the
Mamba-based temporal context module has demonstrated the capability to achieve,
or even surpass, the performance of the latest supervised learning
methodologies, even with a limited amount of labeled data. This study is
expected to establish a new benchmark in sleep stage classification, promising
to guide future research and applications in the field of sleep analysis.

通过结合对比学习任务和掩蔽预测任务，利用未标记的单通道睡眠脑电图（EEG）信号，本研究提出了 NeuroNet，一个自监督学习（SSL）框架，通过三个多导睡眠多项生理参数记录（PSG）数据集的广泛实验，展示了超越现有 SSL 方法的优越性能。同时，研究还提出了基于 Mamba 的时间上下文模块，用于捕捉多样性的 EEG 时期之间的关系。结合 NeuroNet 和基于 Mamba 的时间上下文模块，即使在有限的标记数据量下，也能达到甚至超过最新的监督学习方法的性能。该研究有望建立一个关于睡眠阶段分类的新基准，为睡眠分析领域的未来研究和应用提供指导。