EEG-based Sleep Staging with Hybrid Attention
This work addresses the problem of accurate sleep staging for clinical and research applications, though it appears incremental as it builds on existing networks with a novel attention mechanism.
The paper tackled the challenge of capturing spatial and temporal relationships in EEG signals for sleep staging by proposing the HASS framework with a spatio-temporal attention mechanism, which significantly improved typical sleep staging networks on the MASS and ISRUC datasets.
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.