Abstracts

RATIONALE: An important issue in epileptology is the question whether epileptic seizures can be anticipated. Recent studies have shown that certain measures derived from the theory of nonlinear time series analysis are to some extend capable of extracting information from the EEG that allows the characterization of a pre-ictal state and its distinction from the interictal state. In particular, we have shown a significant loss of phase synchronization to be a characteristic feature of the pre-ictal state. In the present study we investigate some problems and pitfalls that can arise when applying an anticipation technique based on this pre-ictal drop in phase synchronization to the EEG recorded continuously over several days.
METHODS: Showing exemplary segments of the synchronization profiles calculated from the continuous EEG recordings from our patients, we first describe characteristic features of the pre-ictal state and try to distinguish this state from the interictal state. We put a particular emphasis on phenomena occurring during sleep. These segments are then compared to the entire profiles, which in turn are scanned for correlation to changes in AED level and vigilance states during this period of time. Finally, the possible influence of a priori knowledge such as best channel selection is examined.
RESULTS: Examination of sleep phases revealed an increase in phase synchronization during slow-wave sleep (as determined by elevated delta power). Furthermore, certain epochs of distinct anticorrelation appeared to occur predominantly during sleep. Regarding the entire recording length, there appears to be an influence of AED levels on the general level of phase synchronization. All of the above phenomena are likely to result in a decrease in sensitivity and/or specificity of an anticipation technique. In addition, the performance of such an algorithm seems to heavily rely on the a priori knowledge of a best channel combination.
CONCLUSIONS: Findings indicate that a number of phenomena, namely slow-wave sleep, anticorrelation epochs, AED levels and selection of channels may have a strong influence on phase synchronization levels and predictive performance, respectively, that needs to be taken into account when designing an algorithm for seizure anticipation.
[Supported by: This work was supported by the Deutsche Forschungsgemeinschaft.]