Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network

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.Conclusion 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.