A Hybrid Deep Learning Model for Sleep Staging with Multi-Domain Feature Fusion from Single-Channel EEG

Automatic sleep staging typically requires multi-channel EEG data, limiting its application in portable devices. To address this, we propose a hybrid deep learning model that utilizes multi-domain features from single-channel EEG data collected via polysomnography (PSG). Our model employs two feature extractors to capture time-domain and time-frequency-domain features, which are fused for final predictions. Validated on the Haaglanden Medisch Centrum Sleep Centre Database (HMC) with EEG data from 151 subjects, the model achieves an accuracy of 0.747 and an F1 score of 0.742. Compared to state-of-the-art methods, it shows improved multi-classification performance, particularly in N3 stage detection. This study highlights the potential of single-channel EEG for accurate sleep staging and the development of portable PSG-based monitoring systems.Clinical Relevance-This study develops a deep learning model for automatic sleep staging only using a single-channel EEG. Our research would be helpful to automatically classify stages during sleep for sleep physicians.