Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) methods show promise in representation learning from unannotated ECG data, they often overlook the clinical knowledge that can be found in reports. This oversight and the requirement for annotated samples for downstream tasks limit eSSL's versatility. In this work, we address these issues with the Multimodal ECG Representation Learning (MERL}) framework. Through multimodal learning on ECG records and associated reports, MERL is capable of performing zero-shot ECG classification with text prompts, eliminating the need for training data in downstream tasks. At test time, we propose the Clinical Knowledge Enhanced Prompt Engineering (CKEPE) approach, which uses Large Language Models (LLMs) to exploit external expert-verified clinical knowledge databases, generating more descriptive prompts and reducing hallucinations in LLM-generated content to boost zero-shot classification. Based on MERL, we perform the first benchmark across six public ECG datasets, showing the superior performance of MERL compared against eSSL methods. Notably, MERL achieves an average AUC score of 75.2% in zero-shot classification (without training data), 3.2% higher than linear probed eSSL methods with 10\% annotated training data, averaged across all six datasets.

翻译：心电图（ECG）作为非侵入性诊断工具，在临床实践中对检测心律失常疾病至关重要。尽管心电图自监督学习（eSSL）方法在利用未标注心电图数据进行表征学习方面展现出潜力，但其常忽视可从报告中获取的临床知识。这一疏漏以及对下游任务标注样本的需求限制了eSSL的通用性。本文提出多模态心电图表征学习（MERL）框架来解决这些问题。通过基于心电图记录及其关联报告的多模态学习，MERL能够通过文本提示实现零样本心电图分类，从而消除下游任务对训练数据的依赖。在测试阶段，我们提出临床知识增强提示工程（CKEPE）方法，该方法利用大语言模型（LLMs）挖掘经专家验证的外部临床知识库，生成更具描述性的提示词，并减少LLM生成内容中的幻觉现象，以提升零样本分类性能。基于MERL，我们在六个公开心电图数据集上开展了首次基准测试，结果表明MERL性能显著优于eSSL方法。值得注意的是，MERL在零样本分类中（无训练数据）平均AUC达75.2%，相比使用10%标注训练数据的线性探测eSSL方法（跨六数据集均值）高出3.2%。