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.
Article
en Zh
| WPRIM
| ID: wpr-772558
Biblioteca responsable:
WPRO
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 algorithm:sparse 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.
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Texto completo:
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Índice:
WPRIM
Asunto principal:
Arritmias Cardíacas
/
Algoritmos
/
Bloqueo de Rama
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Diagnóstico por Imagen
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Redes Neurales de la Computación
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Electrocardiografía
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
Zh
Revista:
Chinese Journal of Medical Instrumentation
Año:
2019
Tipo del documento:
Article