Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research
This addresses the challenge of making millions of paper ECGs usable for clinical research and AI diagnostics, though it is incremental as it builds on existing digitization methods.
The paper tackles the problem of converting paper ECG scans into digital signals for automated diagnostics, achieving a mean signal-to-noise ratio of 19.65 dB on scanned papers and improving on state-of-the-art across various image artifacts.
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.