EEG signal classification based on EMD and SVM / 生物医学工程学杂志
J. biomed. eng
; Sheng wu yi xue gong cheng xue za zhi;(6): 891-894, 2011.
Article
en Zh
| WPRIM
| ID: wpr-359158
Biblioteca responsable:
WPRO
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.
Texto completo:
1
Base de datos:
WPRIM
Asunto principal:
Algoritmos
/
Procesamiento de Señales Asistido por Computador
/
Reconocimiento de Normas Patrones Automatizadas
/
Clasificación
/
Electroencefalografía
/
Epilepsia
/
Máquina de Vectores de Soporte
/
Métodos
Límite:
Humans
Idioma:
Zh
Revista:
J. biomed. eng
/
Sheng wu yi xue gong cheng xue za zhi
Año:
2011
Tipo del documento:
Article