SomnoNet: A Lightweight and Interpretable Framework for Sleep Staging from Single-Channel EEG

Abstract

Automated sleep staging from single-channel electroencephalography (EEG) is attractive for scalable sleep assessment, but practical systems must jointly address accuracy, efficiency, and clinical interpretability. We propose SomnoNet, a hierarchical raw-EEG framework motivated by expert scoring practice. The model first extracts multi-scale local rhythm representations from short temporal chunks and then integrates intra-epoch organization and inter-epoch context using hierarchical temporal modeling. On two large public benchmarks, SomnoNet achieves 80.9\% accuracy, 79.0\% macro-F1, and 0.739 kappa on Physio2018, and 88.0\% accuracy, 80.7\% macro-F1, and 0.831 kappa on SHHS. To support resource-constrained deployment, we further develop SomnoNet-Nano, a frozen-encoder compact variant that reuses the learned morphology encoder and replaces the original temporal stack with a lightweight sequence unit. SomnoNet-Nano contains 0.049M parameters, runs in 29.49 ms per 30-s epoch on an i7-12700F CPU under FP32 inference, and retains 99.5\% and 99.3\% of the full-model accuracy on Physio2018 and SHHS, respectively. Finally, rhythm-aware decision analysis visualizes segment-level model evidence and relates predictions to clinically meaningful EEG patterns. These results suggest that SomnoNet balances predictive performance, compactness, and transparent decision support for single-channel EEG sleep staging.