Tracking Dynamical Transition of Epileptic EEG Using Particle Filter

Abstract

In this work we used the Liley EEG model as a dynamical model of EEG. Two parameters of the model which are candidates for change during an epileptic seizure are defined as new states in state space representation of this dynamical model. Then SIS particle filter is applied for estimating the defined states over time using the recorded epileptic EEG as the observation of the system. A method for fast numerical solution of the nonlinear coupled equation of the model is proposed. This model is used for tracking the dynamical properties of brain during epileptic seizure. Tracking the changes of these new defined states of the model have good information about the state transition of the model (interictal/preictal/ictal) and can be used in online monitoring algorithms for predicting seizures in epilepsy.