Epileptic EEG signal classification based on wavelet packet transform and multivariate multiscale entropy / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 1073-1090, 2013.
Article
Dans Chinois
| WPRIM
| ID: wpr-352111
ABSTRACT
In this paper, a new method combining wavelet packet transform and multivariate multiscale entropy for the classification of epilepsy EEG signals is introduced. Firstly, the original EEG signals are decomposed at multi-scales with the wavelet packet transform, and the wavelet packet coefficients of the required frequency bands are extracted. Secondly, the wavelet packet coefficients are processed with multivariate multiscale entropy algorithm. Finally, the EEG data are classified by support vector machines (SVM). The experimental results on the international public Bonn epilepsy EEG dataset show that the proposed method can efficiently extract epileptic features and the accuracy of classification result is satisfactory.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Traitement du signal assisté par ordinateur
/
Classification
/
Entropie
/
Diagnostic
/
Électroencéphalographie
/
Épilepsie
/
Analyse en ondelettes
/
Méthodes
Type d'étude:
Etude diagnostique
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2013
Type:
Article
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