Application of SVM and wavelet analysis in EEG classification / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 277-279, 2011.
Article
en Zh
| WPRIM
| ID: wpr-306577
Biblioteca responsable:
WPRO
ABSTRACT
We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.
Texto completo:
1
Índice:
WPRIM
Asunto principal:
Clasificación
/
Diagnóstico
/
Electroencefalografía
/
Epilepsia
/
Análisis de Ondículas
/
Máquina de Vectores de Soporte
/
Métodos
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Año:
2011
Tipo del documento:
Article