S u P re ME : A Supervised Pre-training Framework for Multimodal ECG Representation Learning (2025)

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.

## introduction supervised learning methods have proven effective in classifying cardiac conditions using electrocardiogram (ecg), a widely utilized clinical tool for monitoring the heart's electrical activity @xcite . however, these methods typically rely on large-scale, high-quality annotated datasets, which are costly to create and difficult to scale. to reduce dependence on annotations, recent advancements in ecg self-supervised learning (essl) have enabled the extraction of representative features from large-scale unannotated ecgs using contrastive or generative tasks @xcite @xcite . despite their promise, these methods often rely on strong signal-level augmentations that may distort the semantic integrity of the signal and require complex pretext task designs @xcite . multimodal learning approaches @xcite have also been proposed to learn ecg representations by leveraging free-text ecg reports. however, these methods face challenges due to noise in textual data and the complexities of language grammar, which can hinder learning efficiency @xcite . to address these limitations and develop a scalable, simple, and effective ecg pre-training framework, we propose supreme, a supervised pretraining framework for multimodal ecg representation learning. our contributions are threefold: (a) we introduce an automated pipeline that extracts high-quality clinical entities from raw ecg reports using an instruction-tuned llm enriched with domain-specific knowledge (figure 1(a) ). extracted entities are deduplicated and mapped to dataset-specific standardized diagnostic terms using clinician-validated resources (e.g., snomed ct, umls, scp-ecg foot_0 ), forming a dataset-specific global cardiac query list without manual annotation. this process enables scalable, consistent labeling and captures richer semantics than coarsegrained or free-text alternatives. 2 related work ecg supervised learning. ecg supervised learning (esl) methods, using cnns or trans-formers in figure 2 (a-b), achieve high accuracy in cardiovascular disease diagnosis. cnns excel at capturing spatial and temporal patterns in 1d ecg signals or 2d ecg images @xcite @xcite , while transformers use attention mechanisms to model global dependencies @xcite @xcite . despite their strengths, esls rely heavily on large-scale datasets with expert-verified annotations, making them costly and impractical for pre-training tasks @xcite . this dependence limits their scalability and generalizability, particularly when addressing diverse datasets or unseen cardiac conditions. ## methodology supreme extracts structured clinical entities from ecg reports via an instruction-tuned llm (section 3.1) to form cardiac queries, which are fused with ecg signals via cardiac fusion network (cfn) in a shared latent space, enabling zero-shot classification of unseen cardiac conditions without fine-tuning (section 3.3), thus yielding scalable, clinically meaningful representations. ## experiments 4.1 configuration and settings since supreme's ecg backbone is optimized jointly with the cfn rather than via an explicit contrastive loss, part of the diagnostic knowledge is encoded within cross-modal interactions. linear probing on the ecg encoder alone thus cannot fully utilize the rich alignment captured during pre-training. this explains why the full supreme pipeline including query prompts and cfn achieves stronger zero-shot performance, even compared to linear probing with more labeled data. we highlight zero-shot performance as our main evaluation objective, as it aligns with realworld clinical settings where labeled ecg data is scarce and fine-tuning is often impractical. figure 4 presents framework performance across individual datasets. supreme's advantage on the ptb-xl-superclass dataset is minimal, likely due to the dataset's simplicity, as it includes only 5 broad cardiac condition labels (e.g., norm, sttc, mi), making it difficult to differentiate model performance. all frameworks perform poorly on the ptb-xl-form dataset, which focuses on 19 ecg waveform types that do not directly correspond to cardiac conditions, leading to ambiguous associations and reduced performance for all models. to investigate supreme's sensitivity to pretraining scale, we evaluate its zero-shot performance under varying data proportions (figure 5 ). supreme consistently improves with more data and maintains a clear advantage over nonmultimodal essls. remarkably, with only 20% of pre-training data, supreme outperforms all non-multimodal essls using 1% or 10% labeled data for linear probing, and matches the zero-shot performance of the multimodal baseline merl trained on 100% of the pre-training data. moreover, supreme's zero-shot performance with just 20% of data also exceeds merl's linear probing result with 1% labels, highlighting its superior generalization and efficiency under limited supervision. notably, supreme achieves these results with significantly fewer computational resources and shorter training time 6 (appendix a.10.2). ## conclusion we present a novel llm-based method for ecg clinical entity extraction and construct a highquality labeled dataset from mimic-iv-ecg. building on this, we propose supreme, a scalable supervised pre-training framework for multimodal ecg representation learning that fuses ecg signals with fine-grained, standardized medical terminologies rather than free-text reports. its cardiac fusion network (cfn) and simplified clinical knowledge-enhanced prompt engineering (ck-epe) eliminate the need for further fine-tuning, enabling robust zero-shot classification with concise cardiac queries. benchmarked on six downstream datasets, supreme achieves superior zero-shot performance against 11 essls, underscoring both data efficiency and diagnostic precision. our results highlight the value of explicit entity-level supervision over raw text alignment in ecg multimodal learning, providing a strong basis for clinically oriented ecg representation learning. limitations while supreme achieves strong zero-shot performance, several limitations remain. first, the clinical entity extraction relies on a large language model not fine-tuned for cardiology, which may miss rare or ambiguous terms and introduce noise. second, supreme assumes generalization across clinical settings, but real-world data often involve device variability, demographic shifts, and class imbalance. our experiments show lower performance on rare conditions, indicating sensitivity to distribution shift. additionally, because the ecg encoder is trained jointly with cfn rather than via contrastive objectives, its features alone may not always outperform other baselines under linear probing. lastly, most existing ecg baselines are singlemodal and few of them support open zero-shot evaluation (e.g., merl), underscoring the need for clinically motivated zero-shot benchmarks that better reflect practical deployment scenarios and support fairer comparison across methods. ecg report text : \"{ row [" total_report "]}\" """