Classification of multi-lead ECG with deep residual convolutional neural networks

Abstract

Objective. Automatic electrocardiogram (ECG) interpretation based on deep learning methods is attracting increasing attention. In this study, we propose a novel method to accurately classify multi-lead ECGs using deep residual neural networks. Approach. ECG recordings from seven different open databases were provided by PhysioNet/Computing in Cardiology Challenge 2021. All the ECGs were pre-processed to obtain the same sampling rate. The label inconsistency among the databases was corrected by adding or removing specific labels. A label mask was created to filter out potentially incorrectly labelled data. Five models based on deep residual convolutional neural networks were optimized using an asymmetric loss function to classify multi-lead ECGs. Main results. The proposed method achieved an official challenge score of 0.54, 0.52, 0.50, 0.51, and 0.50 on twelve-lead, six-lead, four-lead, three-lead, and two-lead ECG test sets, respectively. These scores were ranked 5th, 3rd, 7th, 5th and 7th, respectively, in the challenge. Significance. The proposed method can correct the differential labeling tendency of databases from different sources and exhibits good generalization for classifying multi-lead ECGs in the hidden test set. The proposed models have the potential for clinical applications.