Pronounced Nonlinear Traits at Epileptic Seizure Onset and Offset
Abrupt qualitative changes of the dynamics of complex systems that consist of a large number of dynamical units like the human brain are governed by nonlinear processes. A typical example of such a phenomenon is the peri-ictal transition of focal onset seizures. However, the reliable extraction of nonlinear features from short data segments is a highly nontrivial task. We apply a recently introduced method of nonlinear signal analysis that requires neither any kind of phase space reconstruction, necessary e.g. for the estimation of the Lyapunov exponent or the correlation dimension, nor the estimation of probability densities, which is mandatory for measures based on information theory like e.g. mutual information. The method is completely data driven and detects with high sensitivity signatures of determinism, distinguishing between regular, chaotic and stochastic dynamics and stands out for its very high temporal resolution. Using a double window approach our method provides a time-frequency resolution, visualizing the time evolution of nonlinear processes along the frequency spectrum without band-pass filtering beforehand. We apply this index to 100 intracranial EEG recordings of 37 patients with temporal lobe epilepsy provided by different institutions. Our hypothesis is that the sudden transition to an epileptic seizure and the abrupt qualitative change at seizure offset occur through nonlinear processes, while the seizure period is mainly governed by linear autocorrelations, which are responsible for spreading epileptic activity and maintaining ictal dynamics. When selecting electrodes near or within the seizure onset zone (SOZ), we observe pronounced features of non-linear determinism in all cases a few seconds before the seizure became visually detectable by specialists. Seizure offset as well as the immediate post seizure period are also characterized by pronounced nonlinear features, while almost no significant signatures of nonlinear features could be detected during the seizure period. The present work reveals nonlinear dynamics leading to epileptic seizures and opens novel perspective for the analysis of the comparison between inter and pre-ictal intervals, seizure offset and the post-seizure period.