On the Validity of Using Probing Stimuli for Seizure Prediction in the Epileptor Model

Abstract

Epilepsy is characterized by transitory recurrent disturbances to the functioning of the brain. Methods of forecasting the occurrence of seizures could alleviate some of the burden of this disease, which is the unpredictability of these events. One of the proposed approaches for achieving this is the use of stimuli that could highlight otherwise unobservable abnormal changes that would alert patients on impending seizures. This work aims to evaluate the value of a probing approach in seizure prediction, using the Epileptor. Composed by five state variables, which represent three coupled ensembles, the Epileptor is a phenomenological model that replicates transitions involved in onset and offset of seizures, as well as electrographical signatures such as fast discharges and spike and wave events. A slow permittivity variable is responsible for very slow timescale dynamics and affects the likelihood of the occurrence of seizure-like events. Stimuli were defined as biphasic pulses applied every two seconds to perturb the system. The model was configured to generate “normal” dynamics and was simulated with and without stimuli, but slowly shifting the excitability parameter towards seizure activity. Features such as statistical moments, Lag-1 autocorrelation and Hjorth parameters are successively extracted from the model output. Results show decreases in feature values (except for Hjorth Complexity and lag-1 autocorrelation, which increase) as the system approaches the transition from normal to ictal activity, only when probing stimuli are used. This confirms the initial hypothesis and encourages further studies on probing approaches for seizure prediction in different models and configurations.