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Detecting Epileptic Seizures Using Abe Entropy, Line Length and SVM Classifier

  • Aya NaserEmail author
  • Manal Tantawi
  • Howida Shedeed
  • Mohamed F. Tolba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

Epilepsy is a 4th prevalent neurological disorder which affects the individuals in all ages around the world. Epilepsy disorder is characterized by the abnormal movements of human muscles, called seizure, as a result of the abnormality in the brain electrical activity. The electroencephalogram (EEG) can serve as a powerful tool for detecting Epilepsy. In this paper, the most commonly used Andrzejak database is utilized for building an automated system for epilepsy detection. Digital Wavelet Transform (DWT) is applied on the segmented EEG signals to extract the five EEG sub-bands (delta, theta, alpha, beta, and gamma). Approximation and Abe entropies along with line length are calculated for the extracted sub-bands. Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel function is used to distinguish between three classes: (1) normal, (2) interictal (seizure free interval), and (3) ictal (during seizure). The best accuracies achieved are 93.75%, 98.75% and 98.125% for normal, interictal and ictal classes respectively. These accuracies are achieved using the combination of both Abe entropy and line length features together.

Keywords

Electroencephalogram (EEG) Epilepsy Seizure Entropies Line length Digital Wavelet Transform (DWT) Support Vector Machine (SVM) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Aya Naser
    • 1
    Email author
  • Manal Tantawi
    • 1
  • Howida Shedeed
    • 1
  • Mohamed F. Tolba
    • 1
  1. 1.Scientific Computing DepartmentFCIS-Ain Shames UniversityCairoEgypt

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