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Mechanistic cardiac electrophysiology models allow for personalizedsimulations of the electrical activity in the heart and the ensuingelectrocardiogram (ECG) on the body surface. As such, synthetic signals possessknown ground truth labels of the underlying disease and can be employed forvalidation of machine learning ECG analysis tools in addition to clinicalsignals. Recently, synthetic ECGs were used to enrich sparse clinical data oreven replace them completely during training leading to improved performance onreal-world clinical test data. We thus generated a novel synthetic databasecomprising a total of 16,900 12 lead ECGs based on electrophysiologicalsimulations equally distributed into healthy control and 7 pathology classes.The pathological case of myocardial infraction had 6 sub-classes. A comparisonof extracted features between the virtual cohort and a publicly availableclinical ECG database demonstrated that the synthetic signals representclinical ECGs for healthy and pathological subpopulations with high fidelity.The ECG database is split into training, validation, and test folds fordevelopment and objective assessment of novel machine learning algorithms.

MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through electrophysiological simulations