Abstract: Electrocardiogram (ECG) which can reflect the health state of human heart is widely used in clinical examination on heart diseases as an important basis. With the increasing number of ECG data, the demand of  the computer-assisted electrocardiogram analysis has become urgent. Electrocardiogram automatic classification as an indispensable technical means of computer aided electrocardiogram analysis has important medical value. However, because of the weakness and low anti-interference of ECG signal, the traditional ECG classification algorithms have the problems of good effect on test set and poor effect in clinical application. So, this paper introduces a ResNet network structure of one-dimensional convolution based on multi-lead two-dimensional structure, increases the diversity of training samples by means of data enhancement such as translation starting point and adding noise, and uses Focal Loss function to optimize the ECG classification model of individual patients. The model uses 20000 complete 8-lead ECG data and a total of 34 types of abnormal ECG events for classification experiments. The results obtained are: F1 score 0.91, accuracy 93.96%, recall rate 87.89%. Experiment results show the proposed algorithm has better ability of deep feature mining and classification, which verifies its effectiveness in arrhythmia classification.

Key words: deep learning, residual network, convolutional neural network(CNN), ECG, data distribution, loss function