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Study of Feature Extraction Algorithms for Epileptic Seizure Prediction Based on SVM

  • Guangteng Wu
  • Zhuoming Li
  • Yu Zhang
  • Xuyang Dong
  • Liang Ye
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

Epilepsy is a common brain disease state, which threatens the safety of patients. So the effective prediction of epilepsy has great significance. To predict the epileptic seizure, energy feature of electroencephalogram (EEG) is extracted by wavelet transformation and power spectral. Then, support vector machine (SVM) is applied to separate the feature data. The research result shows that the energy of frequency band 0.5–8 Hz would rise 2000 s before seizure onset by analyzing inter-ictal and pre-ictal EEG’s wavelet energy. We used relative wavelet energy and SVM to analyze and test 8 patients’ EEG data, and it shows that the algorithm can predict some patients’ seizure onset except a few of patients’ bad behavior. We replace the wavelet with spectral power and use it to extract feature. The predict accuracy is improved by using spectral power and SVM. Comparing to the relative wavelet energy, the result of 6 patients’ test data improved by spectral power.

Keywords

Epileptic seizure prediction Wavelet energy Power spectral SVM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Guangteng Wu
    • 1
  • Zhuoming Li
    • 1
  • Yu Zhang
    • 2
  • Xuyang Dong
    • 3
  • Liang Ye
    • 1
  1. 1.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  3. 3.School of Life Science and TechnologyHarbin Institute of TechnologyHarbinChina

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