ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms

• URL: http://arxiv.org/abs/2412.12139v1
• Date: Mon, 09 Dec 2024 10:19:02 GMT
• Title: ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
• Authors: Alex Lence, Ahmad Fall, Samuel David Cohen, Federica Granese, Jean-Daniel Zucker, Joe-Elie Salem, Edi Prifti,
• Abstract summary: ECGtizer is an open-source tool designed to digitize paper ECGs and recover signals lost during storage. It employs automated lead detection, three pixel-based signal extraction algorithms, and a deep learning-based signal reconstruction module.
• Score: 0.8480931990442769
• License:
• 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 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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