State and parameter estimation of a neural mass model from electrophysiological signals during the status epilepticus

Abstract

Status epilepticus is an emergency condition in patients with prolonged seizure or recurrent seizures without full recovery between them. The pathophysiological mechanisms of status epilepticus are not well established. With this argument, we use a computational modeling approach combined with in vivo electrophysiological data obtained from an experimental model of status epilepticus to infer about changes that may lead to a seizure. Special emphasis is done to analyze parameter 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 signals. It was observed that during basal activity (before pilocarpine administration) the parameters presented a standard deviation below 30% of the mean value, while during SE activity, the parameters presented variations larger than 200% of the mean value with respect to basal state. The ratio of excitation-inhibition, increased during SE activity by 80% with respect to the transition state, and reaches the lowest value during cessation. In addition, a progression between low and fast inhibitions before or during this condition was found. This method can be implemented in real time, which is particularly important for the design of stimulation devices that attempt to stop seizures. These changes in the parameters analyzed during seizure activity can lead to better understanding of the mechanisms of epilepsy and to improve its treatments.

Keywords: Closed loop stimulation; Cubature Kalman filter; Electrical stimulation device; Epilepsy; Neural mass model; Pilocarpine; Population model; Real time parameter estimation; Status epilepticus.