Abstracts

RATIONALE: Linear and nonlinear time series analyses are gaining an increasing relevance in characterizing the EEG and in understanding the epileptogenic process. The inherent nonstationarity in EEG time series, however, forces almost all analysis techniques to be applied in a moving window fashion using a window length short enough to approximate quasi-stationarity. Here we investigate whether the quantification of nonstationarity on large time scales allows further insight into the epileptogenic process.
METHODS: To quantify nonstationarity we examine long-term changes of EEG dynamics by comparing the recurrence of points in a reconstructed state space to the statistical behavior of recurrence in time series of stationary processes. We apply this technique to intracranial EEG recordings from patients with mesial temporal lobe epilepsy.
RESULTS: Even on a time scale of several minutes, the majority of EEG time series recorded outside the epileptogenic area can be regarded as stationary except for statistical fluctuations. In contrast, EEG time series recorded within the epileptogenic area exhibit a higher degree of nonstationarity even in the absence of high-amplitude epileptiform potentials.
CONCLUSIONS: Our data suggest that the epileptogenic process leads to an increase in nonstationarity in EEG time series. Characterizing nonstationarity can reveal additional information about the epileptogenic process and thus might be valuable for a further improvement of the presurgical evaluation.
Support: Deutsche Forschungsgemeinschaft