Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research

TL;DR

This paper presents an open-source, automated framework for converting paper ECG scans into digital signals, enabling large-scale clinical research and diagnostics from existing paper archives.

Contribution

The authors developed and validated a modular, fully automated algorithm that digitizes paper ECGs with high accuracy, outperforming previous methods and released as open-source software.

Findings

Mean signal-to-noise ratio of 19.65 dB on scanned ECGs

Outperforms state-of-the-art in various artifact conditions

Validated on large datasets with thousands of images

Abstract

Millions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics. We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications. The framework is validated on 37,191 ECG images with 1,596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65 dB on scanned papers with common artifacts. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 35,595 images, including images with perspective distortion, wrinkles, and stains. The model improves on the state-of-the-art in all subcategories. The full software is released as open-source, promoting reproducibility and further development. We hope the software will contribute to unlocking retrospective ECG archives and democratize access to AI-driven diagnostics.