Temporal distribution of seizures in epilepsy.

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 less than 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) a positive R-test (P less than 0.001); (2) deviation from the fitted Poisson distribution towards clustering; and (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.