CHINESE JOURNAL OF COMPUTATIONAL PHYSICS ›› 2015, Vol. 32 ›› Issue (6): 709-714.

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Classification of Epilepsy Based on Lempel-Ziv Complexity and EMD

XIA Deling1, MENG Qingfang1, NIU Hegong2, WEI Yingda1, LIU Haihong1   

  1. 1. School of Information Science and Engineering, University of Jinan, Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan 250022, China;
    2. Qingdao Technological University, College of Automobile and Transportation, Qingdao 266520, China
  • Received:2014-11-24 Revised:2015-02-07 Online:2015-11-25 Published:2015-11-25

Abstract: Taking non-stationary and nonlinearity of epilepsy signals into consideration, we proposed a method for detection of epilepsy, based on Lempel-Ziv (LZ) complexity and empirical mode decomposition (EMD). EMD first decomposed epilepsy signals into a set of intrinsic mode functions (IMFs). Then calculated complexity of each IMF. Bonn dataset was utilized for evaluating the method. Experimental results showed that the highest accuracy could be achieved to 95. 25%. It has advantages of high accuracy, strong adaptability and so on.

Key words: Lempel-Ziv complexity, empirical mode decomposition(EMD), epilepsy signals, accuracy

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