A Probabilistic Closed-Loop Framework for Network Stabilization in Severe Epilepsy

Epilepsy, particularly in its severe and nocturnal forms, is a disorder of dynamic network instability, characterized by unpredictable seizures that impact millions globally. Traditional treatments, relying on static neuromodulation or medication, often fail to provide personalized and adaptive solutions. This white paper presents a novel conceptual framework for epilepsy management, framing seizure generation as a probabilistic state transition within a continuously evolving risk landscape. The framework uses multimodal sensing (EEG, autonomic signals, and behavioral markers) to estimate a time-varying hazard of seizure onset and an associated stability margin. Building on this representation, the framework formulates epilepsy management as a probabilistic control problem, utilizing state-dependent bioelectric neuromodulation and neurochemical micro-interventions to stabilize the system within a safe regime and prevent transitions into ictal states. By shifting from reactive treatment to proactive, personalized care, the system continuously adapts based on individual patient dynamics. This approach integrates insights from epileptology, systems and control theory, and machine learning, positioning the system as a hypothesis-generating, technology-agnostic framework at Technology Readiness Levels 1–2. The primary goal is to provide a structured foundation for future simulation studies, algorithm development, and translational research aimed at personalized, closed-loop epilepsy care, including the prevention of high-risk forms of epilepsy, such as nocturnal epilepsy and SUDEP.