Electroencephalograms in epilepsy: analysis and seizure prediction within the framework of Lyapunov theory

Epileptic seizures are defined as the clinical manifestation of excessive a nd hypersynchronous activity of neurons in the cerebral cortex and represen t one of the most frequent malfunctions of the human central nervous system . Therefore, the search for precursors and predictors of a seizure is of ut most clinical relevance and may even guide us to a deeper understanding of the seizure generating mechanisms. We extract chaos-indicators such as Lyap unov exponents and Kolmogorov entropies from different types of electroence phalograms (EEGs): this covers mainly intracranial EEGs (semi-invasive and invasive recording techniques), but also scalp-EEGs from the surface of the skin. Among the analytical methods we tested up to now, we find that the s pectral density of the local expansion exponents is best suited to predict the onset of a forthcoming seizure. We also evaluate the time-evolution of the dissipation in these signals: it exhibits strongly significant variatio ns that clearly relate to the time relative to a seizure onset. This articl e is mainly devoted to an assessment of these methods with respect to their sensitivity to EEG changes, e.g., prior to a seizure. Further, we investig ate interictal EEGs (i.e., far away from a seizure) in order to characteriz e their more general properties, such as the convergence of the reconstruct ed quantities with respect to the number of phase space dimensions. General ly we use multichannel reconstruction, but we also present a comparison wit h the delay-embedding technique. (C)1999 Elsevier Science B.V. All rights r eserved.