基于复杂网络的癫痫脑电分类与分析

doi:10.6040/j.issn.1672-3961.0.2016.279

Abstract

Abstract: To extract epileptic EEG features in the ictal and interictal period, a method of depicting epileptic EEG was proposed by transforming epileptic EEG time series to epileptic networks. Chaotic multi-dimensional time series coming from the Lorenz system and Rössler system were used to construct a complex network,in which all the variables could be measured. It was found that there was morphological similarity between topology of the complex networks and the attractor of chaotic system. This indicated that complex networks constructed from time series could depict the characteristics of the original signals. For only one measureable variable, multi-dimensional time series were obtained by reconstruction of the phase space. Therefore, the epileptic EEG network was constructed and analyzed in the ictal and interictal period. The results showed that epileptic EEG network topologies in the ictal period were significantly different from that in the interictal period. Meanwhile, the average path length of the network increased significantly and recurrence rates decreased significantly in the ictal period comparing to in the interictal period. These network features could be used to depict the characteristics of EEG time series and could provide the basis for epilepsy automatic identification and prediction.

Key words: morphological similarity, average path length, network topology, complex networks, recurrence rates, epileptic EEG

CLC Number:

TN911.7

HAO Chongqing, WANG Zhihong. Classification and analysis of epileptic EEG based on complex networks[J].JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 8-15.

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