自动将纸质心电图转为数字信号，助力AI诊断。
ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
- 用像素分析和深度学习自动提取与重建心电图信号。
- 在真实疫情数据集上，信号恢复效果优于现有工具。
- 适合想用历史纸质心电图做AI研究的医生或工程师。
心电图（ECG）对心脏疾病诊断至关重要，但传统纸质存储给自动化分析带来挑战。本文提出 ECGtizer，一个开源、全自动的工具，用于数字化纸质心电图并恢复存储中丢失的信号。该工具包含自动导联识别、三种基于像素的信号提取算法及深度学习重建模块。我们在两个数据集（JOCOVID 和 PTB-XL）上评估性能，并与 ECGminer（全自动）和 PaperECG（半自动需人工干预）对比。结果显示，ECGtizer 在信号恢复和临床特征测量保真度上均更优，其 ECGtizerFrag 算法表现最佳。此外，在 GENEREPOL 数据集上，它也支持下游 AI 任务。该工具提升了历史心电图数据的可用性，推动了心电图 AI 分析的发展。
原文摘要 · Abstract (English)
Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analysis. This study introduces ECGtizer, an open-source, fully automated tool designed to digitize paper ECGs and recover signals lost during storage. ECGtizer facilitates automated analyses using modern AI methods. It employs automated lead detection, three pixel-based signal extraction algorithms, and a deep learning-based signal reconstruction module. We evaluated ECGtizer on two datasets: a real-life cohort from the COVID-19 pandemic (JOCOVID) and a publicly available dataset (PTB-XL). Performance was compared with two existing methods: the fully automated ECGminer and the semi-automated PaperECG, which requires human intervention. ECGtizer's performance was assessed in terms of signal recovery and the fidelity of clinically relevant feature measurement. Additionally, we tested these tools on a third dataset (GENEREPOL) for downstream AI tasks. Results show that ECGtizer outperforms existing tools, with its ECGtizerFrag algorithm delivering superior signal recovery. While PaperECG demonstrated better outcomes than ECGminer, it required human input. ECGtizer enhances the usability of historical ECG data and supports advanced AI-based diagnostic methods, making it a valuable addition to the field of AI in ECG analysis.
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