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This paper introduces SleepBand, a novel framework for single-source domain generalization in sleep staging that leverages a physiology-guided approach to enhance model robustness. By utilizing a learnable Morlet filter bank and a structured integration-and-recalibration pipeline, the method effectively anchors representations to domain-invariant sleep rhythms, minimizing the impact of dataset-specific artifacts. SleepBand achieves state-of-the-art performance across five public datasets and demonstrates competitive results in multi-source domain generalization scenarios, highlighting the efficacy of incorporating physiological insights into model design.
SleepBand reveals that embedding physiological priors can significantly enhance the robustness of sleep staging models trained on a single dataset.
Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalization (DG) methods often rely on multiple source domains or domain labels that are rarely available in practice. We tackle the stricter and more practical setting of single-source domain generalization: training on a single labeled source dataset, without domain labels or access to target data. We present SleepBand, a physiology-guided framework that embeds oscillatory priors via a learnable Morlet filter bank and a structured integration-and-recalibration pipeline. This anchors representations to domain-invariant sleep rhythms (e.g., slow waves, spindles), reducing reliance on dataset-specific artefacts. On five public datasets, SleepBand achieves state-of-the-art SDG performance and remains competitive under leave-one-domain-out (multi-source) DG. Analyses show that the learned filters align with canonical neurophysiology and that robustness stems from focusing on narrowband, physiologically meaningful cues. Our results suggest that principled, physiology-aware inductive biases are a promising path for robust single-domain sleep staging. Code is available at https://github.com/lzcn/sleep-band
