Cardiac electrophysiology and arrhythmias

Últimos artículos

• Characterising cardiac tissue properties with graph neural networks

  Ching-En Chiu, Yoo Ri Kim, Magdi Saba, Danilo Mandic, Marta Varela · 18 de agosto de 2026

  Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising ablation targets and improving arrhythmia treatment. We developed a graph neural network-based framework trained on synth…

• Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering

  Xiaoyue Liu, Dongcheng Cang, Xiaohan Yuan, Mark YY Chan, Ching-Hui Sia, Lei Li · 3 de julio de 2026

  Accurate 4D whole-heart mesh reconstruction from sparse cine MRI is critical for creating cardiac digital twins, but remains challenging due to limited 2D slice coverage and the complex coupling between cardiac shape and motion. Existing methods often rely on intermediate contour fitting and typical…

• cAPM: Continual AI-Assisted Pace-Mapping with Active Learning

  Dylan O'Hara, Pradeep Bajracharya, Casey Meisenzahl, Karli Gillette, Anton J. Prassl, Gernot Plank, Saman Nazarian, Roderick Tung, John L Sapp, Linwei Wang · 19 de junio de 2026

  Ventricular tachycardia is a life-threatening rhythm disorder and a major cause of sudden cardiac death. Pace-mapping is a clinical procedure for identifying the intervention target during catheter ablation of VT. It requires clinicians to pace different sites in the ventricles and rapidly interpret…

• Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure

  Ziqi Zhou, Yubo Ye, Sumeet Atul Vadhavka, Linwei Wang, Zhiqiang Tao · 17 de junio de 2026

  Building personalized cardiac electrophysiology (EP) digital twins requires identifying the appropriate model structure for each patient, not merely fitting parameters. Traditional methods rely on experts to manually prescribe hybrid physics-neural architectures, which requires deep domain expertise…

• HAPI-EP: Towards Hybrid, Adaptive, and Predictive Digital Twins of Cardiac Electrophysiology

  Sumeet Vadhavkar, Xiajun Jiang, Yubo Ye, Maryam Toloubidokhti, Linwei Wang · 16 de junio de 2026

  A digital twin (DT) of a patient-specific heart offers significant potential in personalized medicine. However, its rapid and dynamic adaptation to an individual's live data and its predictive capability after adaptation remains central challenges. We examine this challenge from its two building blo…

• Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks

  Paulius Sasnauskas, Yi\u{g}it Yal{\i}n, Goran Radanovi\'c · 9 de junio de 2026

  We study the corruption-robustness of in-context reinforcement learning (ICRL), focusing on the Decision-Pretrained Transformer (DPT, Lee et al., 2023). To address the challenge of reward poisoning attacks targeting the DPT, we propose a novel adversarial training framework, called Adversarially Tra…

• CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations

  Ryan Missel, Xiajun Jiang, Linwei Wang · 8 de junio de 2026

  Personalized virtual heart simulations face challenges in model personalization and computational cost. While neural surrogates offer state-of-the-art solutions, they typically address either efficient personalization or training generalizable models. Recent work reframes this by learning the proces…

• Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins

  Yilin Lyu, Mark YY Chan, Ching-Hui Sia, Lei Li · 2 de junio de 2026

  Accurate 3D geometric characterization of myocardial infarction (MI) is essential for building cardiac digital twins (CDTs) to precisely simulate infarct-related electrophysiology. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is the clinical reference for locating MI, yet its rel…

• Physiology and Anatomy Aware Inverse Inference of Myocardial Infarction for Cardiac Digital Twin

  Mengxiao Wang, Yilin Lyu, Julia Camps, Ching Hui Sia, Mark Yan-Yee Chan, Yanrui Jin, Shuzhi Sam Ge, Chengliang Liu, Lei Li · 22 de mayo de 2026

  Accurate localization of myocardial infarction is essential for risk stratification. While LGE-MRI remains the gold standard, it is resource-intensive. Integrating cine MRI with ECG enables a more detailed representation of infarct properties. Existing inverse MI inference methods overlook realistic…

• Neural Surrogate Forward Modelling For Electrocardiology Without Explicit Intracellular Conductivity Tensor

  Shaheim Ogbomo-Harmitt, Cesare Magnetti, Jakub Grzelak, Oleg Aslanidi · 14 de mayo de 2026

  Accurate forward modelling is essential for non-invasive cardiac electrophysiology, particularly in atrial fibrillation, where electrical activation is highly disorganised. Conventional physics-based forward models require explicit specification of intracellular conductivity tensors, which are not d…

• Benchmarking ECG FMs: A Reality Check Across Clinical Tasks

  M A Al-Masud, Juan Miguel Lopez Alcaraz, Nils Strodthoff · 5 de marzo de 2026

  The 12-lead electrocardiogram (ECG) is a long-standing diagnostic tool. Yet machine learning for ECG interpretation remains fragmented, often limited to narrow tasks or datasets. FMs promise broader adaptability, but fundamental questions remain: Which architectures generalize best? How do models sc…

• Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networks

  Nathan Dermul, Hans Dierckx · 5 de marzo de 2026

  Cardiac arrhythmogenesis is governed by complex electromechanical interactions that are not directly observable in vivo, motivating the development of non-invasive computational approaches for reconstructing three-dimensional activation dynamics. We present a physics-informed neural network framewor…

• Learned Finite Element-based Regularization of the Inverse Problem in Electrocardiographic Imaging

  Manuel Haas, Thomas Grandits, Thomas Pinetz, Thomas Beiert, Simone Pezzuto, Alexander Effland · 10 de febrero de 2026

  Electrocardiographic imaging (ECGI) seeks to reconstruct cardiac electrical activity from body-surface potentials noninvasively. However, the associated inverse problem is severely ill-posed and requires robust regularization. While classical approaches primarily employ spatial smoothing, the tempor…

• A Usable GAN-Based Tool for Synthetic ECG Generation in Cardiac Amyloidosis Research

  Francesco Speziale, Ugo Lomoio, Fabiola Boccuto, Pierangelo Veltri, Pietro Hiram Guzzi · 14 de enero de 2026

  Cardiac amyloidosis (CA) is a rare and underdiagnosed infiltrative cardiomyopathy, and available datasets for machine-learning models are typically small, imbalanced and heterogeneous. This paper presents a Generative Adversarial Network (GAN) and a graphical command-line interface for generating re…

• Synthetic Electrogram Generation with Variational Autoencoders for ECGI

  Miriam Guti\'errez Fern\'andez, Karen L\'opez-Linares, Carlos Fambuena Santos, Mar\'ia S. Guillem, Andreu M. Climent, \'Oscar Barquero P\'erez · 17 de diciembre de 2025

  Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and its clinical assessment requires accurate characterization of atrial electrical activity. Noninvasive electrocardiographic imaging (ECGI) combined with deep learning (DL) approaches for estimating intracardiac electrogr…

• Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models

  Shaheim Ogbomo-Harmitt, Cesare Magnetti, Chiara Spota, Jakub Grzelak, Oleg Aslanidi · 17 de diciembre de 2025

  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. While accurate, these approaches are computationally expensive, limiting their use in real-ti…

• A unified framework for geometry-independent operator learning in cardiac electrophysiology simulations

  Bei Zhou, Cesare Corrado, Shuang Qian, Maximilian Balmus, Angela W. C. Lee, Cristobal Rodero, Marco J. W. Gotte, Luuk H. G. A. Hopman, Mengyun Qiao, Steven Niederer · 2 de diciembre de 2025

  Accurate maps of atrial electrical activation are essential for personalised treatment of arrhythmias, yet biophysically detailed simulations remain computationally intensive for real-time clinical use or population-scale analyses. Here we introduce a geometry-independent operator-learning framework…

• Physics-Informed Neural Operators for Cardiac Electrophysiology

  Hannah Lydon, Milad Kazemi, Martin Bishop, Nicola Paoletti · 12 de noviembre de 2025

  Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology (EP) modelling, remains a significant modelling challenge. Traditional numerical solvers are computationally expensive and sensitive to discretisation, while canonical deep learning methods are data-h…