EEG Signals Classification Using Support Vector Machine
| Autor: | Daoud Boutana, Salah Bourennane, Abdelhakim Ridouh |
|---|---|
| Přispěvatelé: | Faculté des Sciences, Université de Jijel, Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Advanced Science, Engineering and Medicine Advanced Science, Engineering and Medicine, 2020, 12 (2), pp.215-224. ⟨10.1166/asem.2020.2490⟩ Advanced Science, Engineering and Medicine, American Scientific Publishers, 2020, 12 (2), pp.215-224. ⟨10.1166/asem.2020.2490⟩ |
| ISSN: | 2164-6627 |
| DOI: | 10.1166/asem.2020.2490⟩ |
| Popis: | International audience; We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods. |
| Databáze: | OpenAIRE |
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