Seizure Detection in Clinical EEG Based on Multi-feature Integration and SVM

  • Shanshan Chen
  • Qingfang Meng
  • Weidong Zhou
  • Xinghai Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)


Recurrence Quantification Analysis (RQA) was a nonlinear analysis method and widely used to analyze EEG signals. In this work, a feature extraction method based on the RQA measures was proposed to detect the epileptic EEG from EEG recordings. To combine the time-frequency characteristic of epileptic EEG, variation coefficient and fluctuation index were used to analyze epileptic EEG. The multi-feature combination of RQA and linear parameters had better performance in analyzing the nonlinear dynamic characteristics and time-frequency characteristic of epileptic EEG. For features selection and improving the classification accuracy, a support vector machine (SVM) classifier was used. The experimental results showed that the proposed method could classify the ictal EEG and interictal EEG with accuracy of 97.98%.


Epileptic EEG Recurrence quantification analysis (RQA) Multifeature integration Support vector machine (SVM) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shanshan Chen
    • 1
    • 2
  • Qingfang Meng
    • 1
    • 2
  • Weidong Zhou
    • 3
  • Xinghai Yang
    • 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.School of Information Science and EngineeringShandong UniversityJinanChina

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