Mingming Zhao

Accurate sleep staging is crucial for wearable sensor-based sleep monitoring and health interventions. Polysomnography (PSG) signals, rich in information from multiple synchronous sensor channels, are frequently utilized in sleep studies due to their high accuracy in sleep staging. However, employing a wearable PSG for sleep monitoring is uncomfortable, complex, expensive, and impractical. The advent of single-channel wearable electroencephalography (EEG) devices enables comfortable sleep monitoring in ubiquitous scenarios (e.g., home). Despite their advantages, such devices lack sufficient information and have severe accuracy limitations. To improve the accuracy of single-channel EEG-based sleep staging, we propose a method called cross-channel association mining (CCAM). Besides the naive feature extraction, CCAM leverages synchronous single-channel EEG and high-density PSG signals to establish pairwise association feature mining models. These association models can transfer information from other auxiliary PSG channels to the target EEG channel. In inference, CCAM exploits association feature mining models to extract effective features from the target EEG channel and improve sleep staging accuracy through directionally multiview information fusion. Experimental results on three public datasets for sleep staging, ISRUC-S1, ISRUC-S3, and sleep heart health study, show that CCAM outperforms state-of-the-art methods, advancing the field of wearable sensor-based sleep monitoring in ubiquitous scenarios.