arXiv AI June 11, 2026 By Shaheim Ogbomo-Harmitt, Cesare Magnetti, Chiara Spota, Jakub Grzelak, Oleg Aslanidi

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

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Sep 10

Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU

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

benchmarks

arXiv AI

Aug 12

LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space

arXiv:2605. 31249v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging from disease diagnosis to clinical report generation.

By Bosong Huang, Panzhen Zhao, Zengxiang Li, Patricia Lee, Wei Jin, Alan Wee-Chung Liew, Ming Jin, Shirui Pan

diffusion

arXiv Machine Learning

Sep 10

AF-Mamba: Efficient Long-Term Signal Modeling for Early Prediction of Atrial Fibrillation Onset

AF-Mamba is a deep learning model that predicts atrial fibrillation (AF) onset one hour in advance using long‑term RR intervals. It combines temporal convolutional networks for local feature extraction with Mamba, a state‑space model for long‑range sequence modeling, achieving high sensitivity (0.889) and specificity (0.943) in subject‑wise testing. The model maintains strong performance across unseen datasets, offering a favorable trade‑off between predictive accuracy and computational efficiency for real‑time ambulatory monitoring.

By Yongbin Lee, Ki H. Chon

llms benchmarks

arXiv AI

Jul 14

LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification

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

llms efficiency benchmarks

arXiv AI

Jun 2

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

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

benchmarks

arXiv Machine Learning

Jun 2

EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation

arXiv:2605. 29977v2 Announce Type: replace-cross Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by extreme computational demands.

By Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham

efficiency benchmarks safety

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Related stories

Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU

LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space

AF-Mamba: Efficient Long-Term Signal Modeling for Early Prediction of Atrial Fibrillation Onset

LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation