Abstract
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).
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Data availability
The datasets generated and/or analysed during the current study are not publicly available due to Personal Health Information reasons, but are available from the corresponding author on reasonable request.
Code availability
The underlying code for this study, and training/validation datasets, is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.
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Acknowledgements
We would like to thank our participants for their time and energy invested in this study. This study was funded by the National Institutes of Health UG3NS140730 and R01NS131405. The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. We extend gratitude to Medtronic for providing the Summit RC+S system used in this study at no cost.
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C.S. performed hypothesis generation, data collection, conducted analyses, and manuscript writing. M.F.A., J.X.Z., J.Y., and R.A.A. all provided intellectual contributions. J.Y. also contributed to analyses. P.S. performed surgical operations, provided intellectual contributions, and assisted in manuscript writing. S.L. contributed to hypothesis generation, oversight of data collection, intellectual contributions on analysis, and manuscript writing.
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S.L. consults for Iota Biosciences, but declares no non-financial competing interests. All other authors declare no financial or non-financial competing interests.
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Smyth, C., Anjum, M.F., Zhang, JX. et al. Personalized supervised and unsupervised intracranial sleep decoding during deep brain stimulation. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02368-0
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DOI: https://doi.org/10.1038/s41746-026-02368-0
