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

arXiv: https://arxiv.org/abs/2211.15997 · HTML: https://mlsanity.com/paper/2211.15997/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 68 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation](https://mlsanity.com/paper/2106.12387/) (2021)
- [Building Statistical Shape Spaces for 3D Human Modeling](https://mlsanity.com/paper/1503.05860/) (2015)
- [A bi-atrial statistical shape model for large-scale in silico studies of human atria: model development and application to ECG simulations](https://mlsanity.com/paper/2102.10838/) (2021)

## Built on by (most cited first, 6 of 6)

- [Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey](https://mlsanity.com/paper/2406.11445/) (2024)
- [PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation](https://mlsanity.com/paper/2506.10351/) (2025)
- [Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning](https://mlsanity.com/paper/2502.06134/) (2025)
- [FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis](https://mlsanity.com/paper/2509.08961/) (2025)
- [Are ECGs enough? Deep learning classification of pulmonary embolism using electrocardiograms](https://mlsanity.com/paper/2503.08960/) (2025)
- [Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals](https://mlsanity.com/paper/2511.05973/) (2025)

## Abstract (arXiv)

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.