EEG-based Sleep Staging with Hybrid Attention

• URL: http://arxiv.org/abs/2305.09543v1
• Date: Tue, 16 May 2023 15:37:32 GMT
• Title: EEG-based Sleep Staging with Hybrid Attention
• Authors: Xinliang Zhou, Chenyu Liu, Jiaping Xiao and Yang Liu
• Abstract summary: We propose a novel framework called Hybrid Attention EEG Sleep Staging (HASS) Our proposed framework alleviates the difficulties of capturing the spatial-temporal relationship of EEG signals during sleep staging.
• Score: 4.718295968108302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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