Multi-Label Abnormality Classification from 12-Lead ECG Using A 2D Residual U-Net

This paper proposes a two-dimensional (2D) deep neural network (DNN) model for the electrocardiogram (ECG) abnormality classification, which effectively utilizes the inter and intra-lead information comprised in the 12-lead ECG. The proposed model is designed using a stack of residual U-shaped (ResU) blocks so that it can effectively capture ECG features in a multi-scale. The 2D features extracted by the ResU block are down-mixed to 1D features using a lead combiner block designed to merge features of the lead domain into both the time and channel domain. Through experiments, we confirm that our model outperforms other state-of-the-art models in various metrics. The code is made publicly available at https://github.com/seorim0/ResUNet-LC.