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

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. 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 show that ECGtizer outperforms state-of-the-art methods, with its ECGtizer Frag algorithm delivering superior signal recovery performance. While PaperECG demonstrated better outcomes than ECGminer , it also requires 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. • Introduced ECGtizer, an open source and fully automated tool to digitize paper ECGs. • ECGtizer outperforms current methods in signal recovery. • Tested on real-world and public datasets. • Supports advanced AI diagnostic methods. • Opens new research avenues by allowing the use of ECG historical data.