ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
Alex Lence, Ahmad Fall, Samuel David Cohen, Federica Granese,, Jean-Daniel Zucker, Joe-Elie Salem, Edi Prifti

TL;DR
ECGtizer is an open-source, fully automated pipeline that digitizes paper ECGs and recovers lost signals, enabling improved AI-based analysis of historical ECG data.
Contribution
It introduces ECGtizer, a novel fully automated tool combining lead detection, pixel-based signal extraction, and deep learning for signal reconstruction, outperforming existing methods.
Findings
ECGtizer achieves superior signal recovery compared to existing tools.
ECGtizer outperforms semi-automated methods requiring human intervention.
The tool enhances analysis of historical ECG data for AI diagnostics.
Abstract
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…
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Taxonomy
TopicsECG Monitoring and Analysis
