Abstracts

Rationale: Electrical stimulation of the brain (ESB) is used in FDA-approved approaches for patients with drug resistant epilepsy (DRE), but current neuromodulatory approaches are largely palliative, difficult to optimize, and do not target psychiatric or cognitive comorbidities. We developed and implemented a digital health system for long-term tracking of interictal epileptiform activity, seizures, behavior, cognition, and mood comorbidities to improve the management of patients with DRE by streaming data from implanted and wearable devices, patient reported measures of sleep, cognition, and mood. Real-time analytics provides accurate seizure diaries, interictal epileptiform spike rates, dense behavioral tracking, and automated patient specific adaptive stimulation.

Methods: Pre-clinical testing was completed in 13 pet and research canines with epilepsy implanted with the investigational Medtronic Summit RC+S^TM system (bilateral hippocampus & anterior nucleus of the thalamus), with wireless connectivity to a mobile device and cloud computing resource and tested over an average of 800 days of monitoring. We recently obtained an FDA investigational device exemption (IDE) to investigate 10 patients with mesial temporal DRE. The Epilepsy Patient Assist Device was used to acquire continuous intracranial EEG, data from wearable devices, and patient annotations in four patients. The data are synchronized on a cloud-based digital health system providing AI powered large-scale data management and analytics for physicians to review data, create gold standard labels, and automatically retrain algorithms.

Results: Four DRE patients with mesial temporal lobe epilepsy have been implanted with the RC+S^TM. In continuous monitoring we acquired and analyzed over 32 months of data including iEEG, seizure and medication diaries from patients, and sleep and behavioral assessments. The Digital Epilepsy Dashboard provided a seamless interactive interface between patient, engineers, and physicians for tracking behavioral data, brain state, and tuning adaptive ESB to optimize therapy.

Conclusions: The Digital Health System provides a personalized approach to adaptively optimize and track epilepsy treatments in patients, and in the future should prove useful for a wide range of neurological and psychiatric diseases.

Funding: Please list any funding that was received in support of this abstract.: This work was funded in part by NIH NINDS grants UH2/UH3 NS095495 and R01-NS92882.