State and Parameter Estimation of a Neural Mass Model from Electrophysiological Signals during Induced Status Epilepticus

Epilepsy is a brain disorder characterized by transitions from normal (interictal) activity to seizure activity (ictal). These transitions are unpredictable and little is known about the mechanisms that triggers them. In this article we use a computational modelling approach combined with in vivo electrophysiological data obtained from pilocarpine model of epilepsy to infer about changes that may lead to a seizure, special emphasis is done in analyzing parameters changes during or after pilocarpine administration. A cubature Kalman filter is utilized to estimate parameters and states of the model in real time from the observed electrophysiological signal.