| Titre : | A comparison study on EEG signal classification using Component analysis (PCA, ICA) and support vector machine (SVM) |
| Auteurs : | Hadjer Azli, Auteur ; Mourad Adnane, Directeur de thèse |
| Type de document : | texte imprimé |
| Editeur : | [S.l.] : [s.n.], 2017 |
| Format : | 55 f. / ill. / 30 cm. |
| Accompagnement : | 1 CD-ROM. |
| Note générale : |
Mémoire de Master : Electronique : Alger, Ecole Nationale Polytechnique : 2017
Bibliogr. f. 48-49. - Annexes f. 50-55 |
| Langues : | Anglais |
| Index. décimale : | Ms13017 |
| Tags : | Electroencephalogram (EEG) Discrete Wavelet Transform (DWT) Independent Component Analysis (ICA) Principal (PCA) Support Vector Machine (SVM) Epileptic Seizure |
| 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. |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité | Spécialité | Etat_Exemplaire |
|---|---|---|---|---|---|---|
| Ms13017 | Papier + ressource électronique | Bibliothèque Annexe | Mémoire de Master | Disponible | Electronique | Consultation sur place/Téléchargeable |
Documents numériques (1)
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AZLI.Hadjer.pdf URL
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