@inproceedings{cai-etal-2025-supreme,
    title = "{S}u{P}re{ME}: A Supervised Pre-training Framework for Multimodal {ECG} Representation Learning",
    author = "Cai, Mingsheng  and
      Jiang, Jiuming  and
      Huang, Wenhao  and
      Liu, Che  and
      Arcucci, Rossella",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.633/",
    doi = "10.18653/v1/2025.findings-emnlp.633",
    pages = "11817--11844",
    ISBN = "979-8-89176-335-7",
    abstract = "Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (ECG) is critical for diagnosing and monitoring cardiac health, but obtaining large-scale annotated ECG datasets is labor-intensive and time-consuming. Recent ECG Self-Supervised Learning (eSSL) methods mitigate this by learning features without extensive labels but fail to capture fine-grained clinical semantics and require extensive task-specific fine-tuning. To address these challenges, we propose $\textbf{SuPreME}$, a $\textbf{Su}$pervised $\textbf{Pre}$-training framework for $\textbf{M}$ultimodal $\textbf{E}$CG representation learning. SuPreME is pre-trained using structured diagnostic labels derived from ECG report entities through a one-time offline extraction with Large Language Models (LLMs), which help denoise, standardize cardiac concepts, and improve clinical representation learning. By fusing ECG signals with textual cardiac queries instead of fixed labels, SuPreME enables zero-shot classification of unseen conditions without further fine-tuning. We evaluate SuPreME on six downstream datasets covering 106 cardiac conditions, achieving superior zero-shot AUC performance of 77.20{\%}, surpassing state-of-the-art eSSLs by 4.98{\%}. Results demonstrate SuPreME{'}s effectiveness in leveraging structured, clinically relevant knowledge for high-quality ECG representations."
}