Automated Diagnosis of Reduced-Lead Electrocardiograms Using a Shared Classifier

Abstract

Portable ECG devices with a reduced number of leads are increasingly being used in clinical practice. As part of the PhysioNet/Computing in Cardiology Challenge 2021, this study aims to develop an algorithm for automated diagnosis of reduced-lead ECGs. We compared separate baseline classifiers for the different lead-subsets with our newly proposed shared classifier. The different models were pre-trained on a physician-annotated dataset of 269,72612-lead ECGs. Fine-tuning was done on the challenge dataset, consisting of 88,243 ECGs. Even though different models showed promising results on the internal pre-training dataset, optimal scores were achieved by the baseline model on the hidden test set. Our team, UMCU, received scores of 0.47, 0.40, 0.41, 0.41, and 0.41 (ranked 14th, 17th, 17th, 17th, and 16th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set.