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

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 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.