ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram

2023/06/21 by Jungwoo Oh, Gyubok Lee, Oh, Jungwoo +7 · 18 citations
Computer Science · #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning in Healthcare #Quantitative Methods (q-bio.QM) #Signal Processing (eess.SP) #Text Readability and Simplification #Topic Modeling #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2306.15681

openalex publication_date 2023/06/21 · openalex created_date 2023/06/30 · openalex updated_date 2026/07/28

Abstract

Question answering (QA) in the field of healthcare has received much attention due to significant advancements in natural language processing. However, existing healthcare QA datasets primarily focus on medical images, clinical notes, or structured electronic health record tables. This leaves the vast potential of combining electrocardiogram (ECG) data with these systems largely untapped. To address this gap, we present ECG-QA, the first QA dataset specifically designed for ECG analysis. The dataset comprises a total of 70 question templates that cover a wide range of clinically relevant ECG topics, each validated by an ECG expert to ensure their clinical utility. As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs. In addition, we have conducted numerous experiments to provide valuable insights for future research directions. We believe that ECG-QA will serve as a valuable resource for the development of intelligent QA systems capable of assisting clinicians in ECG interpretations. Dataset URL: https://github.com/Jwoo5/ecg-qa

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