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Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network / 中国医疗器械杂志
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: 1 Índice: WPRIM Asunto principal: Arritmias Cardíacas / Algoritmos / Bloqueo de Rama / Diagnóstico por Imagen / Redes Neurales de la Computación / Electrocardiografía Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: Zh Revista: Chinese Journal of Medical Instrumentation Año: 2019 Tipo del documento: Article
Texto completo: 1 Índice: WPRIM Asunto principal: Arritmias Cardíacas / Algoritmos / Bloqueo de Rama / Diagnóstico por Imagen / Redes Neurales de la Computación / Electrocardiografía Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: Zh Revista: Chinese Journal of Medical Instrumentation Año: 2019 Tipo del documento: Article