Protocol-aware dual-view single-channel EEG sleep staging with improved N1 discrimination on public sleep-EDF and SHHS benchmarks

Objective To develop and evaluate DualViewSeqSleepNet, a protocol-aware single-channel EEG model for automatic sleep staging under reproducible public benchmark settings. Methods The model integrates raw-waveform and spectral encoders, gated epoch-level fusion, lightweight bidirectional cross-epoch aggregation, and an auxiliary loss targeting Wake–N1 and N1–N2 transitions. Primary testing used the exact Sleep-EDF SC-subset protocol of SeriesSleepNet. Secondary no-retuning evaluation used SHHS-1 healthy C4-A1 data. Results On Sleep-EDF, DualViewSeqSleepNet achieved macro- F 1 0.821, accuracy 0.869, and Cohen's kappa 0.808, exceeding the SeriesSleepNet anchor and improving N1 F 1 from 0.506 to 0.535. On SHHS-1, it reached macro- F 1 0.791 and accuracy 0.854 without architecture-family retuning. Ablation and robustness analyses supported the contributions of dual-view fusion, cross-epoch aggregation, and N1-aware supervision. Conclusions DualViewSeqSleepNet provides a modest, protocol-aligned improvement for public single-channel sleep staging, especially near Wake–N1–N2 boundaries, but does not establish external clinical validation or autonomous deployment readiness.