Abstracts

Reliable and early prediction of epileptic seizures would open new routes for therapeutic interventions in patients with pharmacorefractory epilepsy. As seizures are characterized by an abnormal synchronization of neurons, multivariate time series analysis techniques detecting synchronized dynamics in invasive EEG recordings of epilepsy patients are a promising approach. Here, we have investigated two synchronization measures originating from the theory of Nonlinear Dynamics with respect to their potential role for epileptic seizure prediction. Two quantities measuring phase and lag synchronization have been applied to an invasive EEG data pool of 21 patients each with 24 hours of seizure-free recordings and 2-5 pre-seizure periods. Synchronization changes have been examined in different brain structures analyzing focal electrode contacts, i.e. early involved in ictal activity, as well as extra-focal electrode contacts, i.e. not involved in ictal activity or only involved lately during seizure spread. The seizure prediction performance has been assessed by the methodology of the seizure prediction characteristic. The seizure prediction characteristic evaluates sensitivity of a prediction method with respect to its specificity and temporal aspects of a prediction. Comparing with the probability to predict seizures by chance, a test is utilized to decide about the statistical significance. Compared to a random prediction, a significantly better seizure prediction performance has been shown for half of the patients. Both decreasing and increasing synchronization in the EEG dynamics could precede seizures. Regarding the topography of brain structures investigated, a statistical significant superiority of combinations between one focal and one extra-focal electrode contact could be demonstrated for the lag synchronization measure (p[lt]0.05). Preictal changes in the synchronization of the EEG dynamics may offer a chance for epileptic seizure prediction. Our study shows that the seizure prediction performance differs between patients and strongly depends on the analysis technique applied and the brain structure investigated. (Supported by German Federal Ministry of Education and Research (BMBF grant 01GQ0420) and the Deutsche Forschungsgemeinschaft (Ti 315/2-1).)