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Titre: A comparison study on EEG signal classification using Component analysis (PCA, ICA) and Support Vector Machine (SVM)
Auteur(s): Azli, Hadjer
Adnane, Mourad, Directeur de thèse
Mots-clés: Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Independent Component Analysis (ICA)
Principal Component Analysis (PCA)
Support Vector Machine (SVM) Epileptic Seizure
Date de publication: 2017
Résumé: This studyaims to analyze and process Electroencephalogram (EEG)signals using an automated classification method with Support vector machine (SVM), to categorize patient’s seizure: epileptic or non-epileptic. We employed a framework of signal analysis techniques, and we started by applying discrete wavelet decomposition(DWT) on the original signal, followed by extracting a set of statistical features and building the feature matrix. Next, a feature reduction PCA and ICA were explored to represent the data in a new distinct space with reduced dimension. Finally, an SVM algorithm was trained and used upon a set of testing data to be classified: epileptic or not. The performance of classification process due to different methods is presented and compared to show the excellent classification process.
Description: Mémoire de Master : Electronique : Alger, Ecole Nationale Polytechnique : 2017
Collection(s) :Département Electronique

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