Open AccessAn improved synchronous algorithm based on Kendall for analyzing epileptic brain network
Author(s) -
Ziqiang Dong,
Fengzhen Hou,
Jiao Dai,
Xinfeng Liu,
Jin Li,
Jun Wang
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.208705
Subject(s) - computer science , rank (graph theory) , correlation , node (physics) , nonlinear system , algorithm , electroencephalography , degree (music) , construct (python library) , complex network , artificial neural network , artificial intelligence , pattern recognition (psychology) , mathematics , neuroscience , psychology , physics , geometry , structural engineering , combinatorics , quantum mechanics , world wide web , acoustics , engineering , programming language
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.