Epileptic seizures are temporally interdependent under certain conditions

The possibility that seizures may be intercorrelated has not been sufficiently investigated. A handful of studies, the majority based on patient seizure diaries, provide disparate results: some claim that seizures are serially correlated and others that they are random events. This study investigates the effect that a seizure may have on the time of occurrence and severity of subsequent ones in subjects undergoing invasive surgical evaluation.

The Savit-Green statistic, a measure of time series lag dependency, was applied to seizure sequences derived from the ECoGs of 26 epilepsy surgery candidates. Seizure onset times, intensities and durations were obtained using a validated seizure detection algorithm, and from these, inter-seizure intervals (ISI) and severities were computed and their lag dependencies were compared to suitably randomized and amplitude-scaled linear surrogate sets.

The null hypothesis (seizures are uncorrelated) was rejected (p<0.05) for ISI in 12/26 subjects and for seizure severity in 13/26. The temporal correlations spanned up to three preceding seizures and were nonlinear in 7/12 subjects for ISI and in 8/13 for severity. An important finding is that dependencies may be related to the frequency of seizures in the sample.

This study demonstrates that under certain conditions, there are linear and nonlinear seizure dependencies of low order and at small time scales (minutes to hours), for ISI and seizure severity. This observation has important implications for studies of seizure predictability, which de facto treat seizures as independent occurrences. Given the study subjects' conditions, it is not clear if the dependencies reflect innate brain dynamics, drug withdrawal, local trauma or a combination of these.