  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.
