The fully automated, open-source ECG digitization algorithm achieved a mean signal-to-noise ratio of 19.65 dB on scanned paper ECGs and outperformed state-of-the-art methods across all image subcategories.
This open-source algorithm successfully digitizes paper ECGs from both scans and mobile photos, outperforming existing methods and enabling large-scale retrospective ECG analysis for AI-driven diagnostics.
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.
Stenhede et al. (Wed,) conducted a other in ECG digitization (n=37,191). Open-source ECG digitization algorithm vs. State-of-the-art algorithms was evaluated on Mean signal-to-noise ratio (SNR). The fully automated, open-source ECG digitization algorithm achieved a mean signal-to-noise ratio of 19.65 dB on scanned paper ECGs and outperformed state-of-the-art methods across all image subcategories.