medRxiv : the preprint server for health sciences · 2026
A Reproducible Framework for Integrating Chronic Deep Brain Stimulation Sensing with Wearable Behavioral Monitoring.
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Chamarthi Saipravallika, Reyes Gabriel, Fraczek Tomasz M, Pouya Sophia, Zhou Yewen, Kutcher Thomas P, Hanish Rick R, Deng Ashley, Herron Jeffrey A, Storch Eric A, Goodman Wayne K, Sheth Sameer A, Provenza Nicole R
Аннотация
OBJECTIVE: Chronic sensing-enabled deep brain stimulation (DBS) devices enable long-term neural recordings in naturalistic settings, but interpreting these data requires concurrent behavioral context. Our objective was to develop a reproducible end-to-end framework for continuous acquisition and synchronization of wearable-derived behavioral data alongside chronic DBS recordings to enable longitudinal neurobehavioral studies.
APPROACH: We developed a publicly available software framework that includes automated wearable data ingestion, neural artifact handling, epoch-based temporal synchronization, and generation of analysis-ready neurobehavioral datasets. We tested this framework using simultaneous sensing-enabled DBS and Oura Ring recordings from three participants with obsessive-compulsive disorder.
MAIN RESULTS: Using this framework, we synchronized 6,384 hours of intracranial neural recordings and wearable-derived data. The resulting multimodal neurobehavioral datasets spanned months of ambulatory monitoring and integrated chronic neural recordings with sleep-wake state, physical activity, autonomic physiology, and DBS parameters. Benchtop testing revealed 12 seconds of Medtronic Percept clock drift relative to network time over a one-week period. This temporal error was substantially smaller than the 10-minute sampling interval of the chronic neural recordings, supporting reliable alignment with wearable-derived data.
SIGNIFICANCE: This work provides an open-source, reproducible framework for continuous acquisition, synchronization, and analysis of wearable-derived data alongside sensing-enabled DBS recordings. By reducing the technical barriers to generating behaviorally annotated neural datasets, this framework enables scalable longitudinal neurobehavioral studies and provides practical foundation for biomarker discovery.
APPROACH: We developed a publicly available software framework that includes automated wearable data ingestion, neural artifact handling, epoch-based temporal synchronization, and generation of analysis-ready neurobehavioral datasets. We tested this framework using simultaneous sensing-enabled DBS and Oura Ring recordings from three participants with obsessive-compulsive disorder.
MAIN RESULTS: Using this framework, we synchronized 6,384 hours of intracranial neural recordings and wearable-derived data. The resulting multimodal neurobehavioral datasets spanned months of ambulatory monitoring and integrated chronic neural recordings with sleep-wake state, physical activity, autonomic physiology, and DBS parameters. Benchtop testing revealed 12 seconds of Medtronic Percept clock drift relative to network time over a one-week period. This temporal error was substantially smaller than the 10-minute sampling interval of the chronic neural recordings, supporting reliable alignment with wearable-derived data.
SIGNIFICANCE: This work provides an open-source, reproducible framework for continuous acquisition, synchronization, and analysis of wearable-derived data alongside sensing-enabled DBS recordings. By reducing the technical barriers to generating behaviorally annotated neural datasets, this framework enables scalable longitudinal neurobehavioral studies and provides practical foundation for biomarker discovery.