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

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.