Detection of epileptic spike wave in EEG signals based on morphological component analysis / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 710-723, 2013.
Article
Dans Chinois
| WPRIM
| ID: wpr-352181
ABSTRACT
This paper proposed a morphological component analysis (MCA) method, which is based on sparse representation, to detect the spike wave in electroencephalogram (EEG) signals. It takes the advantage of MCA being able to extract the background waves and the spike waves from the EEG signals, respectively,as the dictionaries and chooses the discrete cosine transform (DCT) and the daubechies order 4 wavelet (db4) transformation as the dictionaries of MCA to detect the spike waves from the epileptic EEG. The experiment results showed that the MCA could detect epileptic spike waves in EEG signals very effectively, and it yielded high selectivity of 89.01% and sensitivity of 90.71%. As a feature extraction/decomposition algorithm, MCA can be used to extract the spike waves from EEG signals.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Algorithmes
/
Traitement du signal assisté par ordinateur
/
Classification
/
Analyse en composantes principales
/
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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