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arXiv · 2502.17900

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

Che Liu·Cheng Ouyang·Zhongwei Wan·Haozhe Wang·Wenjia Bai·Rossella Arcucci
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Abstract

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.

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Che Liu, Cheng Ouyang, Zhongwei Wan, Haozhe Wang, Wenjia Bai, Rossella Arcucci. 2025-02-25. Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs. https://arxiv.org/abs/2502.17900

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