ECG-image-database: large-scale paired ECG images and time-series with real-world artifacts; a foundation for computerized ECG digitization and analysis
4 affiliations
- 1Emory University
- 2Georgia Institute of Technology
- 3Akershus University Hospital
- 4University of Oslo
Abstract
Objective. We present electrocardiogram (ECG)-Image-Database, comprising ECG image datasets paired with the underlying time-series signals. The database is designed to reflect real-world digitization challenges by incorporating diverse scanning, imaging, and physical artifacts commonly encountered in clinical and archival ECG records. It currently includes ECG images of records from Emory Healthcare (Atlanta, USA), the PTB-XL dataset from Physikalisch-Technische Bundesanstalt (Berlin, Germany), and Akershus University Hospital (Lørenskog, Norway). Approach. ECG images from Emory Healthcare and PTB-XL were generated using ECG-Image-Kit, an open-source toolkit that converts 12-lead ECG time series into realistic printout-style images. The toolkit introduces controlled digital distortions, including noise, wrinkles, stains, and perspective changes. A total of 1977 ECG records (977 PTB-XL and 1000 Emory Healthcare) were processed to create high-fidelity ECG printouts. These images were further subjected to physical degradation—such as soaking, staining, and mold exposure—before being scanned or photographed under varying lighting and capture conditions. In addition, we captured mobile photographs of the original ECG images displayed on computer monitors. This process resulted in 35 595 images. Independently, ECG images from Akershus University Hospital were captured from thermal-paper ECG printouts from 266 patients acquired during routine clinical care. Each printout underwent natural handling degradation and was repeatedly scanned and photographed, yielding 1596 images. Main results. ECG-Image-Database contains 37 191 images derived from 2243 ECG records across three countries. The dataset spans clean scans, noisy photographs, and severe physical deterioration, and includes heterogeneous ECG layouts and acquisition conditions. Each record is linked to its corresponding digital ECG time series, enabling supervised training and benchmarking of digitization and classification methods. Subsets of this resource were used in the PhysioNet Challenge 2024 and a subsequent 2025–2026 Kaggle challenge on ECG image digitization and classification. The database is structured for scalability and future expansion. Significance. By providing paired ECG images and signals across diverse real-world artifact conditions, ECG-Image-Database supports robust development of methods for recovering ECG time series from images and for direct image-based interpretation, helping preserve diagnostic value from non-digital ECG archives.
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BibTeX
@article{reyna2026ecgimagedatabase,
title = {{ECG-image-database: large-scale paired ECG images and time-series with real-world artifacts; a foundation for computerized ECG digitization and analysis}},
author = {Matthew A. Reyna and Deepanshi Deepanshi and James Tyler Weigle and Zuzana Koscova and Kiersten S. Campbell and Kshama Kodthalu Shivashankara and Soheil Saghafi and Sepideh Nikookar and Mohsen Motie-Shirazi and Yashar Kiarashi and Salman Seyedi and Mohammadsina Hassannia and Agnar Martin Bjørnstad and Elias Stenhede and Arian Ranjbar and Gari D. Clifford and Reza Sameni},
journal = {Physiological Measurement},
year = {2026},
month = jul,
doi = {10.1088/1361-6579/ae85b2},
url = {https://doi.org/10.1088/1361-6579/ae85b2},
note = {CC BY},
}
RIS
TY - JOUR
TI - ECG-image-database: large-scale paired ECG images and time-series with real-world artifacts; a foundation for computerized ECG digitization and analysis
AU - Matthew A. Reyna
AU - Deepanshi Deepanshi
AU - James Tyler Weigle
AU - Zuzana Koscova
AU - Kiersten S. Campbell
AU - Kshama Kodthalu Shivashankara
AU - Soheil Saghafi
AU - Sepideh Nikookar
AU - Mohsen Motie-Shirazi
AU - Yashar Kiarashi
AU - Salman Seyedi
AU - Mohammadsina Hassannia
AU - Agnar Martin Bjørnstad
AU - Elias Stenhede
AU - Arian Ranjbar
AU - Gari D. Clifford
AU - Reza Sameni
PY - 2026
DA - 2026/07/02
JO - Physiological Measurement
DO - 10.1088/1361-6579/ae85b2
UR - https://doi.org/10.1088/1361-6579/ae85b2
AB - Objective. We present electrocardiogram (ECG)-Image-Database, comprising ECG image datasets paired with the underlying time-series signals. The database is designed to reflect real-world digitization challenges by incorporating diverse scanning, imaging, and physical artifacts commonly encountered in clinical and archival ECG records. It currently includes ECG images of records from Emory Healthcare (Atlanta, USA), the PTB-XL dataset from Physikalisch-Technische Bundesanstalt (Berlin, Germany), and Akershus University Hospital (Lørenskog, Norway). Approach. ECG images from Emory Healthcare and PTB-XL were generated using ECG-Image-Kit, an open-source toolkit that converts 12-lead ECG time series into realistic printout-style images. The toolkit introduces controlled digital distortions, including noise, wrinkles, stains, and perspective changes. A total of 1977 ECG records (977 PTB-XL and 1000 Emory Healthcare) were processed to create high-fidelity ECG printouts. These images were further subjected to physical degradation—such as soaking, staining, and mold exposure—before being scanned or photographed under varying lighting and capture conditions. In addition, we captured mobile photographs of the original ECG images displayed on computer monitors. This process resulted in 35 595 images. Independently, ECG images from Akershus University Hospital were captured from thermal-paper ECG printouts from 266 patients acquired during routine clinical care. Each printout underwent natural handling degradation and was repeatedly scanned and photographed, yielding 1596 images. Main results. ECG-Image-Database contains 37 191 images derived from 2243 ECG records across three countries. The dataset spans clean scans, noisy photographs, and severe physical deterioration, and includes heterogeneous ECG layouts and acquisition conditions. Each record is linked to its corresponding digital ECG time series, enabling supervised training and benchmarking of digitization and classification methods. Subsets of this resource were used in the PhysioNet Challenge 2024 and a subsequent 2025–2026 Kaggle challenge on ECG image digitization and classification. The database is structured for scalability and future expansion. Significance. By providing paired ECG images and signals across diverse real-world artifact conditions, ECG-Image-Database supports robust development of methods for recovering ECG time series from images and for direct image-based interpretation, helping preserve diagnostic value from non-digital ECG archives.
SN - 0967-3334
SN - 1361-6579
ER -