RefineSleepNet: Task-Driven Signal-Domain Transformation for EEG/EOG Sleep Staging

Automatic sleep staging relies on effective representation of physiological signals. Representative raw-signal sleep-staging encoders typically transform input waveforms while progressively compressing their temporal resolution within the same encoding path. However, explicit modality-specific, sample-aligned, and task-driven signal-domain transformation before epoch-level feature extraction remains comparatively underexplored. To investigate this design, we propose RefineSleepNet, an end-to-end sequence-to-sequence framework that explicitly separates these two stages. Separately parameterized Signal Refinement U-Net (SRU-Net) branches first transform EEG and EOG waveforms while preserving the original temporal length, after which Epoch Feature Extractors (EFEs) extract epoch-level representations from the refined signals. Fused Sequence Attention (FSA) then performs multimodal integration and cross-epoch contextual modeling before epoch-wise classification. All stages are jointly optimized using the final sleep-staging objective without a separate waveform reconstruction target. RefineSleepNet was evaluated on Sleep-EDF-39, Sleep-EDF-153, and ISRUC-3 using subject-independent 10-fold cross-validation, achieving accuracy/macro-F1 (MF1) scores of 84.5%/79.8%, 83.5%/78.1%, and 80.3%/78.1%, respectively. Ablation experiments supported the coordinated design, while statistical analysis and intermediate-representation evaluations further examined the robustness and characteristics of the learned transformation. The learned transformation retained broad temporal and spectral organization while introducing local waveform changes and modality-specific spectral redistribution. These results support the staged organization as a viable modeling strategy for EEG/EOG sleep staging.