"Towards Closed-Loop Sleep Monitoring in Parkinson’s Disease: Self-Supervised Learning Strategies for Sleep Stage Classification"

Abstract

Parkinson’s disease (PD) involves severe sleep disturbances that may accelerate neurodegeneration. Closed-loop deep brain stimulation (DBS) is a promising therapeutic solution but requires accurate, real-time sleep-stage classification from subthalamic nucleus signals, a task where conventional models generalize poorly. To address pronounced class imbalance and improve cross-patient generalization, this work introduces a self-supervised transformer framework. The model employs a masked autoencoder strategy, pretrained on large public EEG/ECoG datasets from healthy subjects to learn balanced representations of all sleep stages, thereby improving discrimination of rare classes. While pretraining yielded limited overall improvement, it specifically enhanced N3 stage identification and next-sleep-stage prediction. Contrastive self-supervision (70%) significantly outperformed a reconstruction-based approach (62%). Furthermore, spectral feature extraction proved more effective than temporal CNN features for distinguishing commonly mispredicted stages. Future work will focus on hybrid reconstruction-contrastive losses, incorporating spectral feature usage to forecasting and extending the forecasting horizon to predict multiple subsequent stages for proactive neuromodulation.