  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.
Code and models are available at https://github.com/cheliu-computation/MERL
