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Epileptic Seizure Detection Using a Convolutional Neural Network

  • Bassem BouazizEmail author
  • Lotfi Chaari
  • Hadj Batatia
  • Antonio Quintero-Rincón
Conference paper
Part of the Advances in Predictive, Preventive and Personalised Medicine book series (APPPM, volume 10)

Abstract

The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.

Keywords

Epilepsy CNN Seizure detection EEG 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bassem Bouaziz
    • 1
    Email author
  • Lotfi Chaari
    • 1
    • 2
  • Hadj Batatia
    • 3
  • Antonio Quintero-Rincón
    • 4
  1. 1.MIRACL LaboratoryUniversity of SfaxSfaxTunisia
  2. 2.Digital Research Centre of SfaxSfaxTunisia
  3. 3.IRITUniversity of ToulouseToulouseFrance
  4. 4.Department of BioengineeringInstituto Tecnológico de Buenos Aires (ITBA)Buenos AiresArgentina

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