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

Elias Stenhede, Agnar Martin Bjørnstad, Arian Ranjbar
10/22/2025
cs.CV

No official code found. Best implementation: Ahus-AIM/Open-ECG-Digitizer (106 stars, last commit 2026-01-15). 1 more below.

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.

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Code Implementations(2)

Ahus-AIM/Open-ECG-DigitizerCommunity73%

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

10637Shell, PythonDec 6, 20249 months ago
digitizaitonecgelectrocardiogram
18h32n/Open-ECG-DigitizerCommunity68%

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

30Shell, PythonNov 18, 20259 months ago

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Cite this paper

@article{stenhede2025digitizing,
  title  = {Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research},
  author = {Elias Stenhede and Agnar Martin Bjørnstad and Arian Ranjbar},
  year   = {2025},
  eprint = {2510.19590},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.19590v1}
}

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