Defining a New Measure for Synchronization of Multi-Channel Epileptic Depth-EEG Signals based on Identification of Parameters of a Computational Model

Abstract

There are various methods to measure the value of synchronization of signals. These methods usually do not consider the sources of the signals. Therefore, these methods usually underestimate the coupling phenomena of the sources of the system that generate the signals. The efficiency of these methods depends mainly on the signals, e.g. whether narrow or broad band. But, according to the system equations or at least equations of an appropriate model of the signals, which also consider the sources and the coupling between them, the estimation of the synchronization values would be more accurate. In this paper we represent an approach to obtain the values of synchronization of some synthetic depth-EEG signals according to their relevant state-space equations. The signals under study are produced by a multi-channel model of epileptic EEG signals. The approach is based on identification of the underlying physiological parameters relevant to each channel of EEG signals. Accordingly, the values of coupling of each two signals then are computed through an optimization process and are used as a measure of synchronization. Results show that the proposed algorithm outperforms the nonlinear correlation coefficient (h2), Pearson correlation coefficient (R2) and mean phase coherence methods that up to now are found to be the most efficient algorithms. Since the implied model can be taken as a model of real depth-EEG signals, the proposed algorithm will be useful in obtaining the synchronization of different areas of the hippocampus.