Learning Cardiac Latent Representations in Vectorcardiogram Space

• URL: http://arxiv.org/abs/2605.31249v1
• Date: Fri, 29 May 2026 12:48:12 GMT
• Title: Learning Cardiac Latent Representations in Vectorcardiogram Space
• Abstract summary: Existing methods operate almost exclusively in the observable ECG signal space.<n>We propose learning a unified latent representation of cardiac electrical activity directly in the VCG space.<n>We introduce LVCG, the first general self-supervised representation learning framework designed to operate in this physically grounded latent space.
• Score: 54.12639543496968
• License: http://creativecommons.org/licenses/by/4.0/
• 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. However, existing methods operate almost exclusively in the observable ECG signal space. In practice, the standard twelve-lead ECG represents multiple projections of the same underlying cardiac electrical activity from different spatial orientations. Therefore, representation learning in the ECG space inevitably introduces substantial redundancy, which may lead to spurious correlations and increased risk of overfitting. To address this and motivated by the Frank vectorcardiogram (VCG) model, we propose learning a unified latent representation of cardiac electrical activity directly in the VCG space. We introduce LVCG, the first general self-supervised representation learning framework designed to operate in this physically grounded latent space. By learning view-invariant latent VCG representations rather than lead-specific artifacts, VCG minimizes redundancy and improves generalization. LVCG generally outperforms ECG-space baselines across tasks, demonstrating enhanced robustness and generalization, especially in domain shift settings.

Related papers

• ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning [50.82313761980655]
  ECG-LLM is an ECG-conditioned large language model trained across four cohorts 679,112 ECG studies from 186,409 patients.<n>It is trained on clinically structured question-answer pairs derived from ECG signals, clinical context, CMR, and ECHO.<n>It successfully recovers conventional ECG measurements, such as heart rate, and strongly predicts complex CMR-derived phenotypes.
  arXiv  Detail & Related papers  (2026-07-15T11:06:35Z)
• PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation [51.79176059799321]
  Photoplethysmography is ubiquitous in wearables but lacks ECG-specific diagnostic morphology.<n>Existing methods rely on statistical alignment and data-driven generation.<n>We propose PG-LRF, a physiology-guided latent flow framework.
  arXiv  Detail & Related papers  (2026-05-09T07:39:58Z)
• Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos [53.368813255127115]
  Existing self-supervised methods suffer from a representational mismatch by aligning ECGs to single-view Echos.<n>We propose Echo2ECG, a multimodal self-supervised learning framework that enriches ECG representations with the heart's morphological structure captured in multi-view Echos.<n>We evaluate Echo2ECG as an ECG feature extractor on two clinically relevant tasks.
  arXiv  Detail & Related papers  (2026-03-09T15:39:57Z)
• Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation [52.19347532840774]
  We propose SE-Diff, a novel physiological simulator and experience enhanced diffusion model for ECG generation.<n> SE-Diff integrates a lightweight ordinary differential equation (ODE)-based ECG simulator into the diffusion process via a beat decoder.<n>Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text-ECG semantic alignment.
  arXiv  Detail & Related papers  (2025-11-13T02:57:10Z)
• Reconstructing 12-Lead ECG from 3-Lead ECG using Variational Autoencoder to Improve Cardiac Disease Detection of Wearable ECG Devices [22.76333494370181]
  We propose WearECG, a Variational Autoencoder (VAE) method that reconstructs twelve-lead ECGs from three leads: II, V1, and V5.<n>Our model includes architectural improvements to better capture temporal and spatial dependencies in ECG signals.<n>We fine-tune ECGFounder, a large-scale pretrained ECG model, on a multi-label classification task involving over 40 cardiac conditions.
  arXiv  Detail & Related papers  (2025-10-13T14:14:37Z)
• Self-Alignment Learning to Improve Myocardial Infarction Detection from Single-Lead ECG [30.009709596357478]
  Myocardial infarction is a critical manifestation of coronary artery disease.<n> detecting it from single-lead electrocardiogram (ECG) remains challenging due to limited spatial information.<n>We propose SelfMIS, a simple yet effective alignment learning framework to improve myocardial infarction detection from single-lead ECG.
  arXiv  Detail & Related papers  (2025-09-23T03:54:39Z)
• Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling [50.58126509704037]
  Heartcare Suite is a framework for fine-grained electrocardiogram (ECG) understanding.<n>Heartcare-220K is a high-quality, structured, and comprehensive multimodal ECG dataset.<n>Heartcare-Bench is a benchmark to guide the optimization of Medical Multimodal Large Language Models (Med-MLLMs) in ECG scenarios.
  arXiv  Detail & Related papers  (2025-06-06T07:56:41Z)
• AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings [34.078819572852446]
  Electrocardiogram (ECG) is highly sensitive in detecting acute heart attacks.<n>This paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data.
  arXiv  Detail & Related papers  (2024-11-17T17:32:58Z)
• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal [19.885905393439014]
  We propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals. Based on the structural features, a temporal model is designed to learn the temporal information for various clinical tasks. The proposed method outperforms the baseline model and shows competitive performances compared with task-specific methods in three clinical applications.
  arXiv  Detail & Related papers  (2023-10-01T23:17:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.