Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation · 2019
[Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network].
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Qi Ji, Zhang Ruiqing, Shen Yang, Chang Shijie, Sha Xiangzheng
Аннотация
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.
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.