arXiv AI August 12, 2026 By Bosong Huang, Panzhen Zhao, Zengxiang Li, Patricia Lee, Wei Jin, Alan Wee-Chung Liew, Ming Jin, Shirui Pan

LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space

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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.

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diffusion

arXiv Machine Learning

Jul 16

Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis

arXiv:2607. 01145v2 Announce Type: replace Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.

By Siwon Kim

llms rag computer-vision benchmarks

arXiv Machine Learning

Jul 2

A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data

arXiv:2607. 01145v1 Announce Type: new Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution.

By Siwon Kim

llms rag computer-vision benchmarks

arXiv AI

Jun 12

Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos

arXiv:2603. 08505v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity.

By Michelle Espranita Liman, \"Ozg\"un Turgut, Alexander M\"uller, Eimo Martens, Daniel Rueckert, Philip M\"uller

multimodal benchmarks

arXiv Machine Learning

Aug 14

The Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning

arXiv:2608. 12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences.

By Ahmed Sameh, Ramzi Al-Sharawi, Yogatheesan Varatharajah

llms rag efficiency

arXiv Machine Learning

Aug 14

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation

arXiv:2608. 12944v1 Announce Type: new Abstract: Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, leaving the shared physiology across sensors unexploited.

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llms rag multimodal benchmarks

arXiv AI

Aug 18

Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning

arXiv:2608. 14662v1 Announce Type: cross Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability.

By Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski

multimodal

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