arXiv Machine Learning August 14, 2026 By Hamza Shafiq, Hung Manh Pham, Bin Zhu, Pan Zhou, Jun Hu, Aaqib Saeed

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

5d ago

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 AI

Jun 9

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.

By Jonathan F. Carter, Lionel Tarassenko

llms rag efficiency multimodal 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

Jul 28

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction

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

benchmarks

arXiv AI

Jul 14

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms

arXiv:2607. 09749v1 Announce Type: cross Abstract: Foundation models have recently emerged as a powerful paradigm for learning transferable representations from large scale biomedical data, yet existing approaches for physiological waveforms primarily optimize reconstruction or forecasting objectives that do not explicitly preserve clinically meaningful waveform morphology.

By Saiyang Feng, Yuanyun Zhang, Shi Li

rag multimodal safety

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

Related stories

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

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms

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