MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations
Autor(in); Institution
Gillette, Karli; Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, AUSTRIA; BioTechMed-Graz, Graz, AUSTRIA
Gsell, Matthias A. F.; Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, AUSTRIA
Nagel, Claudia; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, GERMANY
Bender, Jule; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, GERMANY
Winkler, Benjamin; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Williams, Steven E.; King’s College London, London, UK; University of Edinburgh, Edinburgh, UK
Bär, Markus; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin; Institute of Theoretical Physics, Technische Universität, Berlin, GERMANY
Schäffter, Tobias; 8, Medizinphysik und Metrologische Informationstechnik, PTB-Berlin; King’s College London, London, UK; Biomedical Engineering, Technische Universität Berlin, Einstein Centre Digital Future, Berlin, GERMANY
Dössel, Olaf; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, GERMANY
Plank, Gernot; Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, AUSTRIA; BioTechMed-Graz, Graz, AUSTRIA
Loewe, Axel; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, GERMANY
Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.
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Open Access Gold
Rechteinformation
CC BY 4.0 ; Creative Commons Attribution 4.0 License
Themenbereich der Metrologie
Metrologie in der Medizin
Zitierung
Gillette, K., Gsell, M. A. F., Nagel, C., Bender, J., Winkler, B., Williams, S. E., Bär, M., Schäffter, T., Dössel, O., Plank, G., & Loewe, A. (2023). MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations. Scientific Data, 1–19. https://doi.org/10.1038/s41597-023-02416-4
