Large Language Models as Tutors of Clinical Semantics for Learning Cardiac Representations

ECG–language pretraining derives its primary advantage over signal-only self-supervised learning (SSL) by incorporating clinical reports, thereby grounding ECG representation learning in explicit clinical semantics. However, existing methods typically treat text as an auxiliary modality jointly learned with ECG signals, pursuing performance gains through increasingly elaborate cross-modal alignment mechanisms. This paper is driven by a fundamental yet largely overlooked question: does the quality of text representations itself constitute a bottleneck for ECG representation learning? To address this, we fundamentally redesign the ECG–language framework. Instead of relying on conventional text encoders, we employ a frozen large language model (LLM) to directly encode clinical reports, leveraging its extensive knowledge priors to generate superior text representations. Furthermore, we introduce targeted architectural and optimization mechanisms that enable effective ECG representation learning under these fixed textual supervisory signals. Extensive experiments across three widely used public datasets demonstrate that our proposed method outperforms existing approaches, despite relying solely on a simple global alignment mechanism.