Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network / 中国医疗器械杂志
Chinese Journal of Medical Instrumentation
;
(6): 86-89, 2019.
Artigo
em Chinês
| WPRIM
| ID: wpr-772558
ABSTRACT
OBJECTIVE@#To classify Right Bundle Branch Block (RBBB),Left Bundle Branch Block (LBBB) and normal ECG signals automatically.@*METHODS@#The MIT-BIH database was used as experimental data sources.The training set and test set were extracted for training and testing network models.Based on convolutional neural network,this paper proposed the core algorithmsparse connection residual network.Compared the sparse connected residual network with classic network models,then evaluated the recognition effect of the model.@*RESULTS@#The accuracy of the test set the MIT-BIH database was 95.2%,the result is better than classic network models.@*CONCLUSIONS@#The algorithm proposed in this paper can assist doctors in the diagnosis of heart block related disease and place a high value on clinical application.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Arritmias Cardíacas
/
Algoritmos
/
Bloqueio de Ramo
/
Diagnóstico por Imagem
/
Redes Neurais de Computação
/
Eletrocardiografia
Tipo de estudo:
Estudo diagnóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Chinese Journal of Medical Instrumentation
Ano de publicação:
2019
Tipo de documento:
Artigo
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