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
BEAT-Net is a supervised biomimetic framework for ECG diagnosis that incorporates QRS-centered tokenization and a hierarchical architecture mirroring a cardiologist’s workflow. It processes heartbeat sequences through morphological, spatial, temporal, and transformer-based stages, achieving an AUC of 0.924 on large benchmarks while using only 0.7 million parameters. The model outperforms the 39.5‑million‑parameter HeartLang foundation model on morphological form classification and demonstrates superior cross‑dataset generalization with only 35% of the training data.
By Runze Ma, Haonan Lyu, Shunbo Jia, Qiang Yang, Muzi Xu, Jiaqi Zhang, Zihe Luo, Caizhi Liao
arXiv:2601. 00014v2 Announce Type: replace-cross Abstract: Heart failure (HF) affects 11.
By Eran Zvuloni, Ronit Almog, Michael Glikson, Shany Brimer Biton, Ilan Green, Izhar Laufer, Offer Amir, Joachim A. Behar
arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
By Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed
arXiv:2407. 20893v2 Announce Type: replace-cross Abstract: Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments.
By Yinlong Xu, Zitai Kong, Yixuan Wu, Yue Wang, Xiaoqiang Liu, Yingzhou Lu, Jian Wu, Hongxia Xu
arXiv:2609.22631v1 Announce Type: new
Abstract: Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction...
By Mohammad Sadman Tahsin, Haitham Y. Adarbah, Afzel Noore
arXiv:2607. 10784v1 Announce Type: cross Abstract: Deploying deep learning models for automated electrocardiogram classification on resource-constrained wearable devices remains challenging due to high computational costs.
By Yi Zhao, Jiajun Gao, Chenyang Xu, Yuxi Zhou, Hao Wang
arXiv:2609.08992v1 Announce Type: new
Abstract: False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework comb...
By Athanasios Papastathopoulos-Katsaros, Alexandra Stavrianidi, Zhandong Liu
arXiv:2607. 24035v1 Announce Type: cross Abstract: Explainable AI (XAI) is used to assess whether artificial intelligence models rely on meaningful patterns, yet explanations that appear plausible for individual predictions may systematically misrepresent model behavior.
By Nils Gumpfer, Michael Guckert, Samuel Sossalla, Birgit A{\ss}mus, Jennifer Hannig
arXiv:2512. 13765v2 Announce Type: replace-cross Abstract: The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using physics-based models such as the bidomain or monodomain equations.
By Shaheim Ogbomo-Harmitt, Cesare Magnetti, Chiara Spota, Jakub Grzelak, Oleg Aslanidi
arXiv:2607. 03009v1 Announce Type: new Abstract: Background: Foundation models (FMs) trained on large-scale unlabeled physiological data have emerged as a promising paradigm for medical artificial intelligence.
By Beatrice Zanchi, Giuliana Monachino, Alvise Dei Rossi, Luigi Fiorillo, Georgia Sarquella-Brugada, Giulio Conte, Francesca Dalia Faraci
arXiv:2606. 12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment.
By Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng, Shreyank N Gowda