MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through electrophysiological simulations
This addresses the need for labeled ECG data in medical AI by creating a high-fidelity synthetic dataset, though it is incremental as it builds on existing simulation methods.
The authors generated a synthetic database of 16,900 12-lead ECGs using electrophysiological simulations, equally distributed into healthy and 7 pathology classes, to provide ground truth labels for validating machine learning ECG analysis tools and potentially improve performance on real-world clinical data.
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.