EEG-based Sleep Staging with Hybrid Attention
Xinliang Zhou Chenyu Liu Jiaping Xiao Yang Liu
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.