Model-Based Closed-Loop Seizure Suppression: Algorithm Development and Hardware Implementation

The escalating impact of population aging has led to a surge in neurological disorders, particularly epilepsy, posing a significant challenge in contemporary healthcare. Despite the efficacy of conventional treatments, a substantial percentage of seizure patients exhibit refractory responses. Electromagnetic stimulation therapy, particularly closed-loop configurations, offers a promising avenue due to its non-destructive nature. However, the establishment of an individualized computational model for optimizing anti-epileptic electrical stimulation remains a scientific challenge. This research proposes a closed-loop control system, integrating a computational model of seizure discharges and a closed-loop seizure suppression strategy based on the Unscented Kalman Filter (UKF) algorithm. The hardware-in-loop (HIL) platform enables real-time validation of anti-epileptic closed-loop controllers, reducing risks associated with clinical experiments and facilitating preclinical optimization of electrical stimulation.