EEG signal classification based on EMD and SVM / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 891-894, 2011.
Article
Dans Chinois
| WPRIM
| ID: wpr-359158
ABSTRACT
The automatic detection and classification of EEG epileptic wave have great clinical significance. This paper proposes an empirical mode decomposition (EMD) and support vector machine (SVM) based classification method for non-stationary EEG. Firstly, EMD was used to decompose EEG into multiple empirical mode components. Secondly, effective features were extracted from the scales. Finally, the EEG was classified with SVM. The experiment indicated that this method could achieve good classification result with accuracy of 99 % for interictal and ictal EEGs.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Algorithmes
/
Traitement du signal assisté par ordinateur
/
Reconnaissance automatique des formes
/
Classification
/
Électroencéphalographie
/
Épilepsie
/
Machine à vecteur de support
/
Méthodes
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2011
Type:
Article
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