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.
Algorithms ; Arrhythmias, Cardiac ; diagnostic imaging ; Bundle-Branch Block ; diagnostic imaging ; Electrocardiography ; Humans ; Neural Networks (Computer)