| Titre : |
A comparison study on EEG signal classification using Component analysis (PCA, ICA) and support vector machine (SVM) |
| Type de document : |
texte imprimé |
| Auteurs : |
Hadjer Azli, Auteur ; Mourad Adnane, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2017 |
| Importance : |
55 f. |
| Présentation : |
ill. |
| Format : |
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 (eng) |
| Mots-clés : |
Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Independent Component Analysis (ICA)
Principal (PCA)
Support Vector Machine (SVM) Epileptic Seizure |
| Index. décimale : |
Ms13017 |
| 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. |
A comparison study on EEG signal classification using Component analysis (PCA, ICA) and support vector machine (SVM) [texte imprimé] / Hadjer Azli, Auteur ; Mourad Adnane, Directeur de thèse . - [S.l.] : [s.n.], 2017 . - 55 f. : ill. ; 30 cm. + 1 CD-ROM. Mémoire de Master : Electronique : Alger, Ecole Nationale Polytechnique : 2017
Bibliogr. f. 48-49. - Annexes f. 50-55 Langues : Anglais ( eng)
| Mots-clés : |
Electroencephalogram (EEG)
Discrete Wavelet Transform (DWT)
Independent Component Analysis (ICA)
Principal (PCA)
Support Vector Machine (SVM) Epileptic Seizure |
| Index. décimale : |
Ms13017 |
| 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. |
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A comparison study on EEG signal classification using Component analysis (PCA, ICA) and support vector machine (SVM) (2017)
