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Found 3 papers, 3 papers with code
Synthetic ECG Signal Generation using Probabilistic Diffusion Models
In this work, synthetic ECG signals are generated by the Improved DDPM and by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and then compared.
Arrhythmia Classification using CGAN-augmented ECG Signals
We employed two models for ECG generation: (i) unconditional GAN; Wasserstein GAN with gradient penalty (WGAN-GP) is trained on each class individually; (ii) conditional GAN; one Auxiliary Classifier WGAN-GP (AC-WGAN-GP) model is trained on all classes and then used to generate synthetic beats in all classes.
Synthetic ECG Signal Generation Using Generative Neural Networks
Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases.
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