mlsanityData to 7 Oct 2026

Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs

arXiv 2502.17900 ↗First posted 25 Feb 2025cs.LG cs.AIcited by 5

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Abstract (arXiv)

Recent advances in multimodal ECG representation learning center on aligning ECG signals with paired free-text reports. However, suboptimal alignment persists due to the complexity of medical language and the reliance on a full 12-lead setup, which is often unavailable in under-resourced settings. To tackle these issues, we propose **K-MERL**, a knowledge-enhanced multimodal ECG representation learning framework. **K-MERL** leverages large language models to extract structured knowledge from free-text reports and employs a lead-aware ECG encoder with dynamic lead masking to accommodate arbitrary lead inputs. Evaluations on six external ECG datasets show that **K-MERL** achieves state-of-the-art performance in zero-shot classification and linear probing tasks, while delivering an average **16%** AUC improvement over existing methods in partial-lead zero-shot classification.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 66 references that are in the map. First posted on arXiv 25 Feb 2025.

Builds on

25 of 66 references.

References · 25

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13 more
  1. 2024Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogramcited by 84
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTScited by 4
  2. 2026Label-Conditioned Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Curriculum-Gated Contrastive Alignmentcited by 0
  3. 2026Knowledge-Guided Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Label-Conditioned Contrastive Alignmentcited by 0
  4. 2026Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populationscited by 0
  5. 2026SHIFT-M3: Pre-fusion Alignment-based Consistency Screening for Multimodal ECG Record Integritycited by 0