Temporal distribution of seizures in epilepsy

Abstract

A major problem in epileptology is why a seizure occurs at a particular moment in time. An initial step in solving this problem is a detailed analysis of the temporal distribution of seizures. Using methods and theories of stochastic processes, seizure patterns in a group of epileptic outpatients were examined for stationarity, randomness, dependency and periodicity in a prospective study. Sixteen of the 21 seizure diaries included in the study showed stationarity; 2 were non-stationary and 3 inconclusive. Eleven of the 16 stationary diaries were non-Poisson ( P < 0.005), indicating that in the majority of patients seizures did not occur randomly. The most frequently encountered phenomenon was seizure clustering. Clustering was considered when the diaries fulfilled all three criteria: 1. (1) a positive R-test ( P < 0.001); 2. (2) deviation from the fitted Poisson distribution towards clustering; 3. (3) the feature of an autoregressive process in the autocorrelogram plot. Dependency between seizure events was demonstrated in 8 of the 16 stationary diaries, computing first order transition probabilities. A detailed analysis of seizure occurrence is a major step towards a better understanding of the mechanisms underlying seizure precipitation. This is exemplified by our finding of a relation between seizure frequency and the menstrual cycle.