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

翻译：近年来，深度学习与自然语言生成技术的进步显著提升了图像描述生成的能力，使得针对视觉内容生成类人的自动化描述成为可能。本研究将这些描述生成技术应用于生成临床医生风格的心电图（ECG）数据解读。该研究利用现有的、附有医疗专业人员（HCPs）撰写的自由文本报告的心电图数据集作为训练数据。这些报告虽然常存在不一致性，但为自动化学习提供了宝贵基础。我们提出了一种基于编码器-解码器的方法，利用这些报告训练模型以生成对心电图事件的详细描述。这代表了心电图分析自动化领域的重要进展，在零样本分类和自动化临床决策支持方面具有潜在应用价值。该模型在多个数据集上进行了测试，包括单导联和12导联心电图。其性能显著优于Qiu等人提出的当前最优参考模型，METEOR得分达到55.53%，而参考模型得分为24.51%。此外，本文讨论了若干关键设计选择，全面概述了该领域当前面临的挑战与创新。本研究的源代码已在我们的Git仓库中公开：https://git.zib.de/ableich/ecg-comment-generation-public