TIME DEPENDENCIES IN THE OCCURRENCES OF EPILEPTIC SEIZURES

A new method of analysis, developed within the framework of nonlinear dynamics, is applied to patient recorded time series of the occurrence of epileptic seizures. These data exhibit broad band spectra and gene rally have no obvious structure. The goal is to detect hidden internal dependencies in the data without making any restrictive assumptions, such as linearity, about the structure of the underlying system. The b asis of our approach is a conditional probabilistic analysis in a phas e space reconstructed from the original data. The data, recorded from patients with intractable epilepsy over a period of 1-3 years, consist of the times of occurrences of hundreds of partial complex seizures. Although the epileptic events appear to occur independently, we show t hat the epileptic process is not consistent with the rules of a homoge neous Poisson process or generally with a random (IID) process. More s pecifically, our analysis reveals dependencies of the occurrence of se izures on the occurrence of preceding seizures. These dependencies can be detected in the interseizure interval data sets as well as in the rate of seizures per time period. We modeled patient's inaccuracy in r ecording seizure events by the addition of uniform white noise and fou nd that the detected dependencies are persistent after addition of noi se with standard deviation as great as 1/3 of the standard deviation o f the original data set. A linear autoregressive analysis fails to cap ture these dependencies or produces spurious ones in most of the cases .