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Automated interpretation of electrocardiograms (ECG) has garnered significantattention with the advancements in machine learning methodologies. Despite thegrowing interest, most current studies focus solely on classification orregression tasks, which overlook a crucial aspect of clinical cardio-diseasediagnosis: the diagnostic report generated by experienced human clinicians. Inthis paper, we introduce a novel approach to ECG interpretation, leveragingrecent breakthroughs in Large Language Models (LLMs) and Vision-Transformer(ViT) models. Rather than treating ECG diagnosis as a classification orregression task, we propose an alternative method of automatically identifyingthe most similar clinical cases based on the input ECG data. Also, sinceinterpreting ECG as images is more affordable and accessible, we process ECG asencoded images and adopt a vision-language learning paradigm to jointly learnvision-language alignment between encoded ECG images and ECG diagnosis reports.Encoding ECG into images can result in an efficient ECG retrieval system, whichwill be highly practical and useful in clinical applications. More importantly,our findings could serve as a crucial resource for providing diagnosticservices in underdeveloped regions.

Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report