The study demonstrates that contrastive pre‑training of ECG representations using cardiac magnetic resonance (CMR) imaging data can enhance ECG‑based detection of Chagas disease. By aligning an ECG encoder with a CMR embedding space from 63,193 paired UK Biobank examinations, the authors achieved higher AUROC and sensitivity metrics on CODE‑15%, SaMi‑Trop, and PhysioNet/CinC 2025 Challenge datasets compared to an unaligned baseline. The approach shows that imaging‑supervised ECG representations generalize across different populations and resource‑constrained settings.
By Laura Alvarez-Florez, Daniel Uyterlinde, Samuel Ruip\'erez-Campillo, Lukas P. A. Arts, Folkert W. Asselbergs, Fleur V. Y. Tjong
arXiv:2608. 03597v1 Announce Type: new Abstract: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality.
By Amirhossein Taleshinosrati, Yangyang Wang, Atitaya Phoemsuk, Vahid Abolghasemi, Naser Hossein Motlagh, Sadasivan Puthusserypady, Daniel Teichmann, Abdolrahman Peimankar
arXiv:2607. 27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether their internal representations can be faithfully decomposed, clinically interpreted, or reproduced across independent analyses.
By Yixuan Duan, Wei Qiu
arXiv:2606. 10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training.
By Naoki Nonaka, Jun Seita
arXiv:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.
By Jie Lin, Weijie Sun, Sunil V. Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner
The paper presents a new labelled ICU dataset and benchmarks for detecting atrial fibrillation (AF) from electrocardiograms (ECGs). It compares three AI approaches—feature‑based classifiers, deep learning, and ECG foundation models—across Canadian ICU data and the 2021 PhysioNet challenge, finding that ECG foundation models with transfer learning achieve the highest F1 score (0.89). The study demonstrates the feasibility of automated AF monitoring in ICU settings and provides resources for further research.
By Sarah Nassar, Nooshin Maghsoodi, Sophia Mannina, Shamel Addas, Stephanie Sibley, Gabor Fichtinger, David Pichora, David Maslove, Purang Abolmaesumi, Parvin Mousavi