Lence A., Fall Ahmad, Cohen S. D., Granese F., Zucker Jean-Daniel, Salem J. E., Prifti Edi. (2026). ECGTIzER : an open-source, fully automated pipeline for digitization and signal recovery from paper electrocardiograms. Biomedical Signal Processing and Control, 112 (D), p. 108710 [12 p.]. ISSN 1746-8094.
Titre du document
ECGTIzER : an open-source, fully automated pipeline for digitization and signal recovery from paper electrocardiograms
Année de publication
2026
Auteurs
Lence A., Fall Ahmad, Cohen S. D., Granese F., Zucker Jean-Daniel, Salem J. E., Prifti Edi
Source
Biomedical Signal Processing and Control, 2026,
112 (D), p. 108710 [12 p.] ISSN 1746-8094
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 ECGTIzERFrag 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.
Plan de classement
Santé : généralités [050]
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Informatique [122]
Localisation
Fonds IRD [F B010095306]
Identifiant IRD
fdi:010095306
