A leadwise grouping multibranch network for 12-lead ECG classification achieved an AUROC of 0.9599 on the CinC2020 database and 0.9531 on the SPH database, outperforming existing methods.
Does a leadwise grouping multibranch network improve automatic 12-lead ECG classification performance compared to existing methods?
A novel leadwise grouping multibranch network improves automatic 12-lead ECG classification performance with lower computational cost compared to existing methods.
The 12-lead electrocardiogram (ECG) is widely used in the clinical diagnosis of cardiovascular disease, and deep learning has become an effective approach to automatic ECG classification. Generally, current research simply regards the 12-lead ECG signal as an ordinary 2-D array and does not specifically consider the intrinsic relationship between different leads when building the neural networks. However, ECG classes, from the biomedical perspective, mainly show specific patterns on one or several leads rather than all 12 leads, which suggests that it would be more efficient to learn class-specific intrinsic features from corresponding leads. To make use of such domain knowledge, in this study, we present a multilabel 12-lead ECG classification method based on the leadwise grouping multibranch network. A simple yet effective leadwise grouping strategy is proposed to incorporate domain knowledge to ECG classification models. Meanwhile, a multibranch network is designed accordingly, and spatial and temporal features are extracted by a BranchNet for each branch, which corresponds to one lead group. Moreover, an extended focal loss is presented to solve the class imbalance problem for multilabel classification. The proposed method was evaluated on two large-scale real-world ECG databases and yielded values of 0.9599, 0.7920, 0.7490, 0.5537, 0.1282, and 1.5354 for the area under receiver operating characteristic curve (AUROC), area under precision–recall curve (AUPRC), F1, accuracy (Acc), one-error (OE), and coverage (Cove), respectively, on the PhysioNet/Computing in Cardiology Challenge 2020 (CinC2020) database and values of 0.9531, 0.8975, 0.8102, 0.7484, 0.1791, and 0.5392 on the Shandong Provincial Hospital (SPH) database. The results are better than the existing work and are reached with fewer parameters and lower computational cost, demonstrating the effectiveness of the proposed method and leadwise grouping strategy.
Xie et al. (Sat,) conducted a other in Cardiovascular disease (ECG classification). Leadwise grouping multibranch network vs. Existing methods was evaluated on Model performance metrics (AUROC, AUPRC, F1, Accuracy, One-error, Coverage). A leadwise grouping multibranch network for 12-lead ECG classification achieved an AUROC of 0.9599 on the CinC2020 database and 0.9531 on the SPH database, outperforming existing methods.