Multi-label ECG classification using a reduced-lead configuration

Abstract

Cardiovascular disease (CVD) continues to be a global public health challenge, making scalable and reliable diagnostics necessary. Electrocardiogram (ECG) is a cornerstone in the assessment of the heart, but its conventional 12-lead configuration has logistical and economic hurdles to initiate in low-resource settings. We present, to oppose this, a deep learning model for automated ECG classification using a sparse three-lead configuration (Leads I, II, and V2), selected because of their outstanding diagnostic yield. With the CPSP 2018 dataset, we employed a clean preprocessing pipeline to handle noise and baseline drift and thereafter trained on a ResNet-34 convolutional neural network to predict nine distinct cardiac conditions. We achieved excellent class-specific F1 scores of 0.90 for atrial fibrillation (AF), incomplete atrioventricular block (IAVB), left bundle branch block (LBBB), and right bundle branch block (RBBB), which indicate the strength of the model in detection of life-threatening arrhythmias and conduction defects. Our experiments demonstrate that a low-lead setup, combined with a robust deep learning model, can offer robust diagnostic accuracy. With better accessibility without compromising accuracy, our approach presents an appealing solution to enable scalable ECG screening at scale in resource-limited healthcare environments.