DistillSleep: Real-Time, On-Device, Interpretable Sleep Staging from Single-Channel EEG

Study objectives Polysomnography (PSG) is the current gold standard for sleep staging but requires laboratory equipment, multiple sensors, and labor-intensive manual scoring. We developed DistillSleep, a single-channel electroencephalogram (EEG) framework that delivers accurate, real-time, and interpretable sleep staging on resource-constrained devices. Methods DistillSleep consists of (1) a high-capacity teacher model and (2) a 109 k-parameter student model designed for edge deployment. Both incorporate a Multi-Wavelength Pyramid module and Transformer-based architecture to capture intra- and inter-epoch features. Feature- and prediction-level knowledge distillation transfers the teacher’s expertise to the student. Training and evaluation used >10 000 overnight recordings from six cohorts (SHHS1, PhysioNet 2018, DCSM, KISS, SleepEDF-78, ISRUC), following AASM guidelines. Performance was assessed with Macro-F1. Results The teacher achieved state-of-the-art Macro-F1 scores (SHHS1 81.1%, PhysioNet 78.9%, DCSM 81.2%, KISS 80.0%) and provided frequency-resolved saliency maps, inter-epoch context and well-calibrated confidence (ECE 0.07). The student maintained competitive accuracy (up to 79.7% Macro-F1) while executing <10 ms per 30-second epoch on three embedded platforms (Raspberry Pi 4B, Jetson orin nano, Coral dev board), reducing computational load 115-fold versus the best prior method (SleePyCo). Interpretability was transferred intact to the student, offering clinicians frequency-band importance and inter-epoch context visualizations, and calibration was further improved by 2.7×. Conclusions DistillSleep combines expert-level accuracy, millisecond-scale latency, and transparent decision logic in a single-channel EEG form factor. These capabilities pave the way for point-of-care diagnostics, same-night therapy titration, and large-scale home monitoring, expanding the reach of sleep medicine while retaining clinical trust.