  An automatic classification method has been studied to effectively detect and
recognize Electrocardiogram (ECG). Based on the synchronizing and orthogonal
relationships of multiple leads, we propose a Multi-branch Convolution and
Residual Network (MBCRNet) with three kinds of feature fusion methods for
automatic detection of normal and abnormal ECG signals. Experiments are
conducted on the Chinese Cardiovascular Disease Database (CCDD). Through
10-fold cross-validation, we achieve an average accuracy of 87.04% and a
sensitivity of 89.93%, which outperforms previous methods under the same
database. It is also shown that the multi-lead feature fusion network can
improve the classification accuracy over the network only with the single lead
features.
