ECGtizer: An open-source, fully automated pipeline for digitization and signal recovery from paper electrocardiograms

Abstract

Background and Objective:

Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analyses. Despite the growing interest in leveraging AI for ECG analysis, there remains a lack of accessible, fully automated tools for digitizing paper-based ECGs. Existing solutions are often incomplete, behind paywalls, or not suited for large-scale use. To address this gap, we present ECGtizer: an open-source, fully automated tool that enables high-fidelity digitization of paper ECGs, ensuring long-term preservation of clinical data and unlocking their potential for modern AI-driven analysis.

Methods:

ECGtizer employs automated lead detection, three different 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. The tools’ digitization performance was assessed in terms of signal recovery, the fidelity of clinically relevant feature measurement and downstream AI classification tasks on a third dataset (GENEREPOL).

Results:

Results show that ECGtizer outperforms state-of-the-art methods, with its ECGtizer ${}_{\text{Frag}}$ algorithm delivering superior signal recovery performance. While PaperECG demonstrated better outcomes than ECGminer, it also requires human input.

Conclusions:

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.