Optimising Sleep Stage Detection Using a Minimal Non‐ EEG Physiological Signal Set and Deep Learning

ABSTRACT Automatic sleep stage classification is essential for enabling non‐invasive, at‐home monitoring. However, current methods often rely on electroencephalogram (EEG) signals and ad‐hoc development approaches that limit reproducibility. We present a reproducible engineering framework for a deep learning model based on the U‐Net architecture that classifies sleep into five stages (Wake, N1, N2, N3 and REM) or four (Wake, Light Sleep, Deep Sleep and REM) using only three easily acquired physiological signals: oxygen saturation (SpO), heart rate (HR) and abdominal respiratory effort (AbdRes). In contrast to most previous studies, our model provides sleep stage predictions on a per‐second basis, thus overcoming the limitations associated with fixed 30‐s epochs. The model was trained on the Sleep Heart Health Study—Visit 2 (SHHS2) dataset and externally validated on the Multi‐Ethnic Study of Atherosclerosis (MESA). Optimisation of the model was achieved via Keras Tuner with the Hyperband algorithm. The study achieved weighted F1‐scores of 68% (five‐stage) and 71% (four‐stage) with Cohen's Kappa of 0.61 and 0.67 on SHHS2, with consistent performance on MESA. These results demonstrate strong generalisation and suggest that this lightweight, EEG‐free approach offers a practical path towards scalable, clinically relevant sleep monitoring.