RT Journal Article
T1 Reducing leads, enhancing wearable practicality: A comparative study of 3-lead vs. 12-lead ECG classification
A1 González-Cabeza, Sergio
A1 Sanz-Guerrero, Mario
A1 Piñuel, Luis
A1 Buelga Suárez, Mauro Luis
A1 Alonso Salinas, Gonzalo Luis
A1 Diaz-Vicente, Marian
A1 Recas Piorno, Joaquín
AB Inspired by recent advances in clinical research and the growing adoption of wearable ECG devices, this study explores the feasibility of using reduced-lead ECGs for automated detection of heart anomalies using deep learning, providing a more accessible and cost-effective alternative to traditional 12-lead ECGs. This research adapts and evaluates a state-of-the-art 12-lead deep learning model (from Ribeiro et al. [1]) for 3-lead configurations. The 12-lead ECG model architecture was trained from scratch on the public database PTB-XL. It was then modified to use 3 leads by only changing the input layer. Despite a 75% reduction in input data, the 3-lead model showed only a subtle 3% performance drop. To address this gap, the 3-lead model was further optimized using a novel strategy that combines transfer learning and a One-vs-All classification approach. Using PTB-XL's five-class setup (normal vs. four pathologies: myocardial infarction, ST/T change, conduction disturbance, and hypertrophy), we report the micro-averaged F1-score across all test samples. The new optimized 3-lead model achieves a global (micro-averaged) F1-score of 77% (vs. 78% for the 12-lead model). These findings highlight the potential of simplified and cost-effective reduced-lead classification models to deliver near-equivalent diagnostic accuracy. This advancement could democratize access to early cardiac diagnostics, particularly in resource-limited settings.
YR 2025
FD 2025
LK https://hdl.handle.net/20.500.14352/132929
UL https://hdl.handle.net/20.500.14352/132929
LA eng
DS Docta Complutense
RD 26 feb 2026