Abstract

Epilepsy is a chronic disease characterized by recurrent seizures. Epileptic seizures occur due to central nervous system (neurological) disorders. Around 50 million people worldwide suffer from epilepsy. The diagnosis of epilepsy can be done through an electroencephalogram (EEG). There are two important periods to consider in EEG recording, the interictal period (clinically no seizures) and ictal (clinically seizures). Meanwhile, visual inspection of EEG signals to detect interictal and ictal periods often involves an element of subjectivity and it requires experience. So that automatic detection of interictal periods with classification method is badly needed. In this study, Least Square Support Vector Machine (LS SVM) method for classification of interictal and ictal was used. Data preprocessing process was carried out using Discrete Wavelet Transform (DWT). The result showed that the classification using LS SVM with kernel RBF method achieved an accuracy under curve (AUC) of 96.3%.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Pages198-210
Number of pages13
DOIs
Publication statusPublished - 2021

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
Volume76
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

Keywords

  • EEG signal
  • Ictal
  • Interictal
  • Square Support Vector Machine

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