ECG pattern classification by feature searching algorithm based on maximal divergence / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 53-56, 2008.
Artigo
em Chinês
| WPRIM
| ID: wpr-230657
ABSTRACT
This paper presents a method of using feature searching algorithm based on maximal divergence value to get the optimized feature combinations at different dimensions from feature space. Feature space is obtained through wavelet transform on ECG beat. Then the feature vector is determined by analyzing the changes of divergence value of those optimized feature combinations along with the dimensions. BP artificial neural network is trained by the feature vector and four types of ECG beats(normal beat, left bundle branch block beat, right bundle branch block beat and paced beat) obtained from MIT-BIH database are classified with a success of 93.9%.
Texto completo:
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Índice:
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Assunto principal:
Algoritmos
/
Processamento de Sinais Assistido por Computador
/
Bloqueio de Ramo
/
Classificação
/
Redes Neurais de Computação
/
Eletrocardiografia
/
Métodos
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2008
Tipo de documento:
Artigo
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