Personalized supervised and unsupervised intracranial sleep decoding during deep brain stimulation

Impaired sleep in Parkinson's Disease (PD) is a significant unmet need. Targeting sleep stage-specific neurophysiologies with adaptive Deep Brain Stimulation (aDBS) may ameliorate sleep disruption. This study analyzes the efficacy of personalized machine learning approaches on classifying sleep stages from participants receiving deep brain stimulation. We acquired 283 hours of multi-night intracranial cortico-basal recordings with synchronized sleep stage labels derived from scalp EEG across 5 participants during chronic stimulation. Five-stage classification accuracy across PD subjects averaged 80.2% (±0.9% SEM). When constraining sleep classification to algorithms implementable in currently available DBS devices, e.g., binary NREM classification using linear models, an average accuracy of 85.9% (±0.4% SEM) was achieved for PD subjects. Additionally, linear models trained on unsupervised cluster labels achieved an average accuracy of 83.5% (±5.6% SEM) when discriminating NREM sleep. Overall, this demonstrates the feasibility of personalized supervised and unsupervised ML models for sleep classification using intracranial data during stimulation. The Institutional Review Board approved the parent study protocol, and the study was registered on clinicaltrials.gov (NCT0358289; IDE G180097).