Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling

TL;DR

This paper introduces ECG-ReGen, a retrieval-augmented self-supervised model that improves ECG report generation and question answering by combining ECG encoding with large language models, enhancing accuracy and efficiency.

Contribution

The paper presents a novel retrieval-based framework integrating self-supervised ECG encoding with LLM refinement for improved ECG report generation and question answering.

Findings

Superior performance on PTB-XL and MIMIC-IV-ECG datasets.

Effective zero-shot question answering with off-the-shelf LLMs.

Scalable approach for accurate ECG interpretation.

Abstract

Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. Our method leverages a self-supervised learning for the ECG encoder, enabling efficient similarity searches and report retrieval. By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen effectively analyzes ECG data and answers related queries, with the potential of improving patient care. Experiments conducted on the PTB-XL and MIMIC-IV-ECG datasets demonstrate superior performance in both in-domain and cross-domain scenarios for report generation. Furthermore, our approach exhibits competitive performance on ECG-QA dataset compared to fully supervised methods when utilizing off-the-shelf LLMs for zero-shot question answering. This approach, effectively combining self-supervised encoder and LLMs, offers a scalable and efficient solution for accurate ECG interpretation, holding significant potential to enhance clinical decision-making.