SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning

• URL: http://arxiv.org/abs/2502.19668v1
• Date: Thu, 27 Feb 2025 01:29:51 GMT
• Title: SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning
• Authors: Mingsheng Cai, Jiuming Jiang, Wenhao Huang, Che Liu, Rossella Arcucci,
• Abstract summary: We propose $textbfSuPreME, a $textbfSu$pervised $textbfPre$-training framework for representation learning.<n>By using text-based cardiac queries instead of traditional categorical labels, SuPreME enables zero-shot classification of unseen diseases without additional fine-tuning.
• Score: 8.831192046626251
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (ECG) recordings are 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 applies Large Language Models (LLMs) to extract structured clinical entities from free-text ECG reports, filter out noise and irrelevant content, enhance clinical representation learning, and build a high-quality, fine-grained labeled dataset. By using text-based cardiac queries instead of traditional categorical labels, SuPreME enables zero-shot classification of unseen diseases without additional fine-tuning. We evaluate SuPreME on six downstream datasets covering 127 cardiac conditions, achieving superior zero-shot AUC performance over state-of-the-art eSSL and multimodal methods by over 1.96\%. Results demonstrate the effectiveness of SuPreME in leveraging structured, clinically relevant knowledge for high-quality ECG representations. All code and data will be released upon acceptance.

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