An improved synchronous algorithm based on Kendall for analyzing epileptic brain network

Abstract

In this study, we propose a kendall rank correlation based synchronous algorithm inverse rank correlation (IRC). The kendall rank correlation is a generalized algorithm of nonlinear dynamics analysis which can effectively measure nonlinear correlations between variables. The study of complex networks has gradually penetrated into various fields of the social sciences. We use our algorithm to construct functional brain networks based on the data from electroencephalogram (EEG). The average node degree of complex brain networks is analyzed to investigate whether epileptic functional brain networks are distinctly different from normal brain networks. Results show that our method can distinguish between epileptic and normal functional brain networks and needs to record a very small number of EEG data. Experimental data show that our method suited to distinguish between epilepsy and normal brain node degree, which may contribute to further deepening the study of the brain neural dynamic behaviors, and provide an effective tool for clinical diagnosis.