Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, capturing both the spatial and temporal relationships within electroencephalogram (EEG) signals during different sleep stages remains challenging. In this paper, we propose a novel framework called the Hybrid Attention EEG Sleep Staging (HASS) Framework. Specifically, we propose a well-designed spatio-temporal attention mechanism to adaptively assign weights to inter-channels and intra-channel EEG segments based on the spatio-temporal relationship of the brain during different sleep stages. Experiment results on the MASS and ISRUC datasets demonstrate that HASS can significantly improve typical sleep staging networks. Our proposed framework alleviates the difficulties of capturing the spatial-temporal relationship of EEG signals during sleep staging and holds promise for improving the accuracy and reliability of sleep assessment in both clinical and research settings.

翻译：睡眠分期对于评估睡眠质量和诊断睡眠障碍至关重要。然而，在不同睡眠阶段中捕捉脑电图信号的空间和时间关系仍然具有挑战性。本文提出了一种新颖的框架，称为混合注意力脑电图睡眠分期（HASS）框架。具体而言，我们设计了一种精心设计的时空注意力机制，根据大脑在不同睡眠阶段的空间-时间关系，自适应地为通道间和通道内脑电图片段分配权重。在MASS和ISRUC数据集上的实验结果表明，HASS能够显著改进典型的睡眠分期网络。我们的框架缓解了睡眠分期中捕捉脑电图信号空间-时间关系的困难，有望提高临床和研究环境中睡眠评估的准确性和可靠性。