Digitizing paper ECGs at scale: an open-source algorithm for clinical research

TL;DR

This paper presents an open-source algorithm that automatically converts paper ECG scans into digital signals for use in clinical research and diagnostics.

Contribution

A novel, modular framework for digitizing paper ECGs at scale, outperforming existing methods.

Findings

The algorithm achieves a mean signal-to-noise ratio of 19.65 dB on paper ECGs with common artifacts.

It outperforms state-of-the-art methods across all subcategories in the Emory Paper Digitization ECG Dataset.

The framework is validated on over 37,000 ECG images from Akershus University Hospital and the Emory dataset.

Abstract

Billions 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 1596 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.

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