Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals

• URL: http://arxiv.org/abs/2605.27583v1
• Date: Tue, 26 May 2026 18:52:57 GMT
• Title: Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals
• Abstract summary: Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis.<n>Recent approaches align ECG signals with clinical reports to incorporate diagnostic semantics, but clinical reports often fail to preserve the rich physiological structure of ECG waveforms.<n>We formulate ECG representation learning from an information-theoretic perspective and derive a tractable objective that jointly preserves signal structure and integrates clinical semantics.
• Score: 34.28801923123035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal approaches align ECG signals with clinical reports to incorporate diagnostic semantics, but clinical reports often fail to preserve the rich physiological structure of ECG waveforms, particularly across multiple levels of abstraction ranging from coarse diagnostic categories to fine-grained morphology. To address this limitation, we formulate ECG representation learning from an information-theoretic perspective and derive a tractable objective that jointly preserves signal structure and integrates clinical semantics. Based on this principle, we propose \textbf{MERIT} (Multimodal ECG Representation via Information Theory), a dual-branch pretraining framework combining masked ECG modeling with ECG--text contrastive alignment. Extensive experiments on PTB-XL and additional benchmarks demonstrate consistent improvements over prior methods, including gains exceeding $3%$ F1 on PTB-XL All and $5%$ F1 on SubClass classification. In zero-shot evaluation, MERIT further improves performance by up to $ +2.66\%$ AUC and $ +2.11\%$ F1 on PTB-XL SubClass, while also demonstrating robustness under multiple distribution-shift settings. Moreover, leveraging the learned ECG representations for ECG-conditioned clinical text generation with large language models improves text quality across several metrics, including ROUGE and METEOR. Together, these results demonstrate that MERIT learns more informative and clinically meaningful ECG representations, particularly for fine-grained clinical applications.

Related papers

• Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations [18.282452158790257]
  Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured observations into diagnostic evidence.<n>We introduce LuminaECG, a clinically structured ECG reasoning framework that reformulates ECG interpretation as measurement-grounded visual reading.<n> ECG signals are rendered on standard electrocardiographic grid paper to preserve the spatial and scale cues used in clinical reading.
  arXiv  Detail & Related papers  (2026-08-10T03:00:35Z)
• ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation [5.4061821164321175]
  ECG report generation could reduce clinicians' interpretive workload, improve diagnostic efficiency, and expand access to cardiac assessment in underserved communities.<n>We propose ECG-LENS, an end-to-end ECG report-generation framework that integrates multi-lead signal modeling, diagnosis-aware representations, and clinically grounded text generation.
  arXiv  Detail & Related papers  (2026-08-06T11:18:31Z)
• 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)
• Learning Cardiac Latent Representations in Vectorcardiogram Space [54.12639543496968]
  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.
  arXiv  Detail & Related papers  (2026-05-29T12:48:12Z)
• CLEF: EEG Foundation Model for Learning Clinical Semantics [50.3767252824646]
  We introduce CLEF, a clinically grounded long-context EEG foundation model.<n>We evaluate CLEF on a new 234-task benchmark spanning disease phenotypes, medication exposures, and EEG findings.
  arXiv  Detail & Related papers  (2026-05-11T16:34:58Z)
• 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)
• Transferring Clinical Knowledge into ECGs Representation [0.19498378931702776]
  We propose a novel three-stage training paradigm that transfers knowledge from multimodal clinical data into a powerful, yet unimodal, ECG encoder.<n>We employ a self-supervised, joint-embedding pre-training stage to create an ECG representation that is enriched with contextual clinical information.<n>As an indirect way to explain the model's output, we train it to also predict associated laboratory abnormalities directly from the ECG embedding.
  arXiv  Detail & Related papers  (2025-12-07T22:19:24Z)
• 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)
• From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining [22.214252217020174]
  We introduce MELP, a novel Multi-scale ECG-Language Pretraining (MELP) model that fully leverages hierarchical supervision from ECG-text pairs.<n>We evaluate MELP on three public ECG datasets across multiple tasks, including zero-shot ECG classification, linear probing, and transfer learning.
  arXiv  Detail & Related papers  (2025-06-11T07:22:17Z)
• 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)
• MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [28.35107188450758]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions.<n>Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation.<n>We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)

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.