Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning

TL;DR

This paper presents a deep learning approach to automatically generate detailed clinical reports from ECG data, improving automation and accuracy in ECG interpretation using encoder-decoder models trained on existing datasets.

Contribution

It introduces a novel encoder-decoder method for ECG report generation that significantly outperforms previous models, advancing automated ECG analysis and clinical decision support.

Findings

Achieved a METEOR score of 55.53%, surpassing the previous 24.51%.

Demonstrated effectiveness on both 1- and 12-lead ECG datasets.

Provided publicly available source code for reproducibility.

Abstract

Recent advances in deep learning and natural language generation have significantly improved image captioning, enabling automated, human-like descriptions for visual content. In this work, we apply these captioning techniques to generate clinician-like interpretations of ECG data. This study leverages existing ECG datasets accompanied by free-text reports authored by healthcare professionals (HCPs) as training data. These reports, while often inconsistent, provide a valuable foundation for automated learning. We introduce an encoder-decoder-based method that uses these reports to train models to generate detailed descriptions of ECG episodes. This represents a significant advancement in ECG analysis automation, with potential applications in zero-shot classification and automated clinical decision support. The model is tested on various datasets, including both 1- and 12-lead ECGs. It significantly outperforms the state-of-the-art reference model by Qiu et al., achieving a METEOR score of 55.53% compared to 24.51% achieved by the reference model. Furthermore, several key design choices are discussed, providing a comprehensive overview of current challenges and innovations in this domain. The source codes for this research are publicly available in our Git repository https://git.zib.de/ableich/ecg-comment-generation-public