ArticleScientific reports2021
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Assessing the Credibility of Quantitative Information: A General Framework.Annals of biomedical engineering · 2026Article
- AI-enabled privacy-preserving cardiac diagnostics via electrocardiograms.Scientific reports · 2026Article
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- End-to-End Platform for Electrocardiogram Analysis and Model Fine-Tuning: Development and Validation Study.Journal of medical Internet research · 2026Article
- Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information.Patterns (New York, N.Y.) · 2025Article
- Synthetic electrocardiograms for Brugada syndrome: from data generation to expert cardiologists evaluation.European heart journal. Digital health · 2025Article
- Evaluating the feasibility of 12-lead electrocardiogram reconstruction from limited leads using deep learning.Communications medicine · 2025Article
- Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students.Nursing reports (Pavia, Italy) · 2024Article
- Data augmentation for generating synthetic electrogastrogram time series.Medical & biological engineering & computing · 2024Article
- ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.Physiological measurement · 2024Article
- Continual learning framework for a multicenter study with an application to electrocardiogram.BMC medical informatics and decision making · 2024Article
- Classification feasibility test on multi-lead electrocardiography signals generated from single-lead electrocardiography signals.Scientific reports · 2024Article
- ECG Synthesis via Diffusion-Based State Space Augmented Transformer.Sensors (Basel, Switzerland) · 2023Article
- Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges.Yearbook of medical informatics · 2023Article
- Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search.Scientific reports · 2023Article
- Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.Diagnostics (Basel, Switzerland) · 2023Article
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18 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.
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