Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 14, Iss. 4, October, 2010, pp. 411-434
@2010 Society for Chaos Theory in Psychology & Life Sciences

 
 
 

Nonlinear Dynamics of Seizure Prediction in a Rodent Model of Epilepsy

Levi B. Good, University of Texas Southwestern Medical Center, Dallas, TX
Shivkumar Sabesan, Arizona State University, Tempe, AZ, and St. Joseph”s Hospital and Medical Center, Phoenix, AZ
Steven T. Marsh, Department of Neurology Research, Barrow Neurological Institute, St. Joseph”s Hospital and Medical Center, Phoenix, AZ
Konstantinos Tsakalis, Arizona State University, Tempe, AZ
David M. Treiman, St. Joseph”s Hospital and Medical Center, Phoenix, AZ
Leon D. Iasemidis, Arizona State University, Tempe, AZ, Mayo Clinic Arizona, Phoenix, AZ

Abstract: Epilepsy is a dynamical disorder with intermittent crises (seizures) that until recently were considered unpredictable. In this study, we investigated the predictability of epileptic seizures in chronically epileptic rats as a first step towards a subsequent timely intervention for seizure control. We look at the epileptic brain as a nonlinear complex system that undergoes spatio-temporal state transitions and the Lyapunov exponents as indices of its stability. We estimated the spatial synchronization or desynchronization of the maximum short-term Lyapunov exponents (STLmax, approximate measures of chaos) among multiple brain sites over days of electroencephalographic (EEG) recordings from 5 rats that had developed chronic epilepsy according to the lithium pilocarpine rodent model of epilepsy. We utilized this synchronization of EEG dynamics for the construction of a robust seizure prediction algorithm. The parameters of the algorithm were optimized using receiver operator curves (ROCs) on training EEG datasets from each rat for the algorithm to provide maximum sensitivity and specificity in the prediction of their seizures. The performance of the algorithm was then tested on long-term testing EEG datasets per rat. The thus optimized prediction algorithm on the testing datasets over all rats yielded a seizure prediction mean sensitivity of 85.9%, specificity of 0.180 false predictions per hour, and prediction time of 67.6 minutes prior to a seizure onset. This study provides evidence that prediction of seizures is feasible through analysis of the EEG within the framework of nonlinear dynamics, and thus paves the way for just-in-time pharmacological or physiological inter-ventions to abort seizures tens of minutes before their occurrence.

Keywords: Epilepsy, Seizure Prediction, EEG, Dynamic Synchronization, Lyapunov Exponents