ABSTRACT
Epileptic seizures are not randomly occurring events, but are instead the product of nonlinear dynamics in brain circuits, and are expected to be detectable with some antecedence (Le Van Quyen et al. 2001). Indeed, it has long been observed that the transition from the interictal state (far from seizures) to the ictal state (seizure) is not sudden and may be preceded from minutes to hours by preictal clinical, metabolic, or electrical changes(Lehnertz et al. 2007). In recent years, new answers to this question have begun to emerge from quantitative analyses of the electroencephalogram (EEG). Most current approaches use quantities that draw inferences about the level of EEG complexity, such as an effective correlation dimension (Lehnertz and Elger 1998), correlation density (Le Van Quyen et al. 2001), or Lyapunov exponents (Sackellares et al. 1999). More recently introduced, bivariate measures that estimate dynamical interactions between two time series of two EEG channels such as phase synchronization or other measures for generalized synchronization were especially promising for seizure prediction (Mormann et al. 2003; Le Van Quyen et al. 2005). Even if the existence of a preictal period is not completely con–rmed, these observations strongly suggest that distinct electrical characteristics can be determined between preictal and interictal periods. Nevertheless, until now, a physiological interpretation of these changes remains unknown. A few studies have hypothesized that changes in interictal epileptiform spikes can anticipate the pathophysiological recruitments that give rise to a seizure. However, in animal models of focal epilepsy, no consistent
25.1 Introduction .......................................................................................................................... 357 25.2 Materials and Methods ......................................................................................................... 358
25.2.1 EEG Database and Preprocessing ............................................................................ 358 25.2.2 Detection of Epileptic Spikes ................................................................................... 359
25.2.2.1 Wavelet Transform ..................................................................................... 359 25.2.2.2 Modulus Maxima .......................................................................................360
