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Automatic 12-lead ECG Classification Using Deep Neural Networks

ECG is the most commonly used diagnostic tool for identifying cardiovascular disease. However, manual interpretation of ECG is inefficient and requires medical practitioners with a lot of training. In this work we proposed two deep learning models to classify ECG automatically. One model had a hybrid architecture of convolutional neural network and recurrent neural network. The other model contained deep residual neural networks. The output layer of both models was activated by a sigmoid function to get classification results. We manually located all the premature beats in each ECG recording and selected 10 s segments which contained at least one premature beat as training samples. Recordings without premature beats were randomly split into 10 s segments. The models were then trained on these ECG segments for 30 epochs with an optimizer of Adam. After training, the model performance was evaluated on the hidden validation set and test set maintained by the challenge organizers. Our team, nebula, achieved a challenge validation score of 0.526, and full test score of 0.109, but was not ranked due to omissions in the submission. The results show potential application value in automatically classifying 12-lead ECG.