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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
Chinese Journal of Medical Instrumentation ; (6): 86-89, 2019.
Article in Chinese | 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.
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Full text: Available Index: WPRIM (Western Pacific) Main subject: Arrhythmias, Cardiac / Algorithms / Bundle-Branch Block / Diagnostic Imaging / Neural Networks, Computer / Electrocardiography Type of study: Diagnostic study Limits: Humans Language: Chinese Journal: Chinese Journal of Medical Instrumentation Year: 2019 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Arrhythmias, Cardiac / Algorithms / Bundle-Branch Block / Diagnostic Imaging / Neural Networks, Computer / Electrocardiography Type of study: Diagnostic study Limits: Humans Language: Chinese Journal: Chinese Journal of Medical Instrumentation Year: 2019 Type: Article