ECG-Image-Database：包含真实成像与扫描伪影的心电图图像数据集；计算机化心电图图像数字化与分析的基础
ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis
AI总结：
本文介绍了 ECG-Image-Database 数据集，通过 ECG-Image-Kit 工具包对真实 ECG 记录施加数字与物理失真，生成了 35,595 张带伪影的图像及基准时间序列，为开发稳健的 ECG 图像数字化与分类模型奠定了基础。
摘要：
我们介绍了 ECG-Image-Database，这是一个从心电图（ECG）时间序列数据生成的大型且多样化的 ECG 图像集合，包含真实的扫描、成像和物理伪影。我们使用开源 Python 工具包 ECG-Image-Kit，从原始 ECG 时间序列生成 12 导联 ECG 打印件的真实图像。这些图像包含数字和物理方式生成的噪声、褶皱、污渍和透视偏移等真实失真。该工具包被应用于来自 PTB-XL 数据库的 977 份 12 导联 ECG 记录和来自 Emory Healthcare 的 1,000 份记录，以创建高保真合成 ECG 图像。这些独特的图像既经历了使用 ECG-Image-Kit 的程序性失真，也经历了浸泡、染色和霉菌生长等物理效应，随后在各种光照条件下进行扫描和摄影，以创建真实世界的伪影。\n生成的数据集包含 35,595 张带有广泛成像伪影和失真的软件标记 ECG 图像。该数据集在图像旁提供了基准时间序列数据，为开发用于 ECG 数字化和分类的机器与深度学习模型提供参考。图像质量各异，从干净纸张的清晰扫描到退化纸张的嘈杂照片，从而能够开发出更具泛化能力的数字化算法。\nECG-Image-Database 满足了将基于纸张和非数字 ECG 数字化以进行计算机分析的关键需求，为开发能够将 ECG 图像转换为时间序列的稳健机器与深度学习模型奠定了基础。该数据集旨在作为 ECG 数字化和计算机化标注工作的参考。ECG-Image-Database 已被用于 2024 年 PhysioNet Challenge 的 ECG 图像数字化与分类竞赛。
英文摘要：
We introduce the ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data, with real-world scanning, imaging, and physical artifacts. We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series. The images include realistic distortions such as noise, wrinkles, stains, and perspective shifts, generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These unique images were subjected to both programmatic distortions using ECG-Image-Kit and physical effects like soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts. The resulting dataset includes 35,595 software-labeled ECG images with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the images, offering a reference for developing machine and deep learning models for ECG digitization and classification. The images vary in quality, from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable digitization algorithms. ECG-Image-Database addresses a critical need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of converting ECG images into time-series. The dataset aims to serve as a reference for ECG digitization and computerized annotation efforts. ECG-Image-Database was used in the PhysioNet Challenge 2024 on ECG image digitization and classification.
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