Wafformer: Wave-Frequency Cross-Prediction and Alignment for Knowledge-Enhanced Sleep Representation

Electroencephalogram (EEG)-based sleep stage classification (SSC) identifies human sleep phases from EEG signals. With the integration of deep learning frameworks, automatic SSC has achieved notable accuracy. However, current SSC approaches encounter significant challenges: 1) Data challenge. The high costs associated with collecting and annotating sleep EEG signals result in a limited number of training samples, complicating supervised training for deep models. 2) Knowledge challenge. Incorporating clinical and human insights related to sleep into models for SSC remains challenging. To address these challenges, we propose Wafformer, a novel self-supervised framework to extract knowledge-enhanced sleep representation from EEG signals. Specifically, Wafformer leverages the Transformer architecture, employing self-attention and cross-attention encoders to extract wave-frequency features. We introduce innovative pretext tasks, such as cross-domain wave peaks and frequency-energy predictions, to integrate essential knowledge about salient waveforms associated with sleep states, complemented by temporal contrastive learning to align wave-frequency features. Extensive experiments on ISRUC, SleepEDF-SC, and SleepEDF-ST datasets demonstrate the superior performance of Wafformer on the sleep staging task. Besides, Wafformer can adaptively capture dynamic waveforms related to sleep stages, offering a comprehensive understanding of sleep phenomena.