An Eye Mask-Based Wearable System Toward Tailored Sleep Monitoring

Comfortable and reliable wearable systems for home-based automatic sleep staging are crucial for remotely diagnosing sleep disorders. Among various categories of biosignals, electrooculography (EOG) has emerged as a promising modality due to its ease of acquisition and robustness to noise. However, research on EOG-based sleep staging remains limited, with most studies constrained to the standard 2-channel EOG configuration recommended by the American Academy of Sleep Medicine (AASM) scoring manual. Thus, it may lack higher spatial information, hindering fully capturing the tiny sleep-related eye movements. In this study, we developed a novel eye mask-based EOG acquisition system capable of collecting 14-channel spatially diverse EOG signals. Building upon this hardware innovation, we further proposed a dedicated deep learning model, MTNet, combining multi-scale feature extraction and temporal attention, to fully exploit the rich information from the collected EOG data for accurate sleep staging , which is capable of effectively capturing relevant sleep features from EOG signals. We also evaluated the importance of each EOG channel for sleep staging, providing recommendations for EOG-based data acquisition in-home sleep monitoring systems. Our study involved 24 participants, resulting in 48 overnight sleep recordings. In K-fold cross-validation, 14-channel EOG achieved an average accuracy of 83.2%, a Cohen’s kappa coefficient of 0.77, and an MF1 score of 77.7%. These results surpass the performance of the standard EOG, which yielded an accuracy of 82.3%, a Cohen’s kappa coefficient of 0.76, and an MF1 score of 76.1%. In particular, the 14-channel EOG demonstrated superior detection accuracy in the N1 and REM sleep stages compared to the standard setup. In conclusion, the proposed 14-channel EOG collection system—formed as a wearable eye mask—shows significant potential as a high-dimensional biosignal source for home-based sleep monitoring. By combining 14-channel EOG acquisition with the MTNet model, and investigating the importance of channels, this study marks a substantial step forward in an accurate and user-friendly sleep monitoring solution.