Analysing Epileptic EEG Signals Based on Improved Transition Network

  • Yang Li
  • Yao Guo
  • Qingfang MengEmail author
  • Zaiguo Zhang
  • Peng Wu
  • Hanyong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)


The epileptic automatic detection was very significance in clinical. The nonlinear time series analysis method based on complex network theory provided a new perspective understand the dynamics of nonlinear time series. In this paper, we proposed a new epileptic seizure detection method based on statistical properties of improved transition network. First, we improved the transition network and electroencephalogram (EEG) signal was constructed into the improved transition network. Then, based on the statistical characteristics of improved transition network, the mathematical expectation of node distribution in a network was extracted as the classification feature. Finally, the performance of the algorithm was evaluated by classifying the epileptic EEG dataset. Experimental results showed that the classification accuracy of proposed algorithm is 97%.


Complex network Improved transition network Mathematical expectation Seizure detection EEG 



This work was supported by the National Natural Science Foundation of China (Grant No. 61671220, 61701192, 61640218), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2017QF004).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yang Li
    • 1
    • 2
  • Yao Guo
    • 1
    • 2
  • Qingfang Meng
    • 1
    • 2
    Email author
  • Zaiguo Zhang
    • 3
  • Peng Wu
    • 1
    • 2
  • Hanyong Zhang
    • 1
    • 2
  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingJinanChina
  3. 3.CET Shandong Electronics Co., Ltd.JinanChina

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