Integration of Wearable Biosensors and Agentic AI for Real-Time Seizure Prediction and Autonomic Intervention in Treatment-Resistant Epilepsy

Background: Treatment-resistant epilepsy (TRE) affects approximately 30% of patients with epilepsy, leaving them at high risk for Sudden Unexpected Death in Epilepsy (SUDEP) and physical trauma. Traditional seizure detection systems are reactive, alerting caregivers only after the onset of ictus. This study evaluates a proactive "Agentic AI" framework that utilizes wearable biosensors to provide real-time seizure prediction and trigger preventative autonomic interventions.

Methods: 120 patients with TRE were monitored over 12 months using a multi-modal wearable system (EEG, HRV, and Electrodermal Activity). An Agentic AI model, trained on patient-specific longitudinal data, was deployed to provide a "Probability of Ictus" ($P_{ict}$) score.

Results: The system achieved a mean sensitivity of 91.2% with a median lead time of 12.4 minutes. False Discovery Rates (FDR) were maintained at 0.04 per 24 hours. Crucially, the integration of a closed-loop Vagus Nerve Stimulation (VNS) trigger reduced the severity of seizures by a mean of 42% on the National Hospital Seizure Severity Scale (NHS3).

Conclusion: Agentic AI, combined with multi-modal wearables, transforms epilepsy management from a reactive to a predictive model, significantly improving patient safety and quality of life.

Keywords: Epilepsy, Physical Trauma, Wearable Biosensors, Seizures.