The feature extraction method of EEG signals based on degree distribution of complex networks from nonlinear time series

  • Authors:

  • Fenglin Wang;Qingfang Meng;Weidong Zhou;Shanshan Chen

  • Affiliations:

  • School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;School of Information Science and Engineering, Shandong University, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China

  • Venue:
  • ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
  • Year:
  • 2013

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Abstract

The nonlinear time series analysis method based on complex networks theory gives a novel perspective to understand the dynamics of the nonlinear time series. Considering the electroencephalogram (EEG) signals showing different nonlinear dynamics under different brain states, this study proposes an epileptic EEG analysis approach based on statistical properties of complex networks and applies the approach to epileptic EEGs automatic detection. Firstly, the complex network is constructed from the epileptic EEG signals and the degree distribution (DDF) of the resulting networks is calculated. Then the entropy of the degree distribution (NDDE) is used as a feature to classify the ictal EEGs and the interictal EEGs. The experiment results show that the NDDE of the ictal EEG is lower than interictal EEG's and the classification accuracy, taking the NDDE as a classification feature, is up to 96.25%.