ArticleScientific data2023
MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Explainable deep learning-based classification of Wolff-Parkinson-White electrocardiographic signals.Frontiers in physiology · 2026Article
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.Current cardiology reports · 2024Review
- BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping.Computers in biology and medicine · 2024Article
- Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.Journal of the American Heart Association · 2024Review
- Ten quick tips for electrocardiogram (ECG) signal processing.PeerJ. Computer science · 2024Article
- Stretch of the papillary insertion triggers reentrant arrhythmia: anFrontiers in physiology · 2024Article
- Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation.BMC medical research methodology · 2023Article
- PTB-XL+, a comprehensive electrocardiographic feature dataset.Scientific data · 2023Article
- Benchmarking privacy and utility in synthetic tabular cohorts for Alzheimer's disease research.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Virtual population-based analysis of anatomical and electrophysiological risk markers for atrial fibrillation.JRSM cardiovascular diseaseArticle
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Authors and funding
11 authors at 4 institutions in 3 countries.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.