  Generative Adversarial Networks (GAN) are known to produce synthetic data
that are difficult to discern from real ones by humans. In this paper we
present an approach to use GAN to produce realistically looking ECG signals. We
utilize them to train and evaluate a denoising autoencoder that achieves
state-of-the-art filtering quality for ECG signals. It is demonstrated that
generated data improves the model performance compared to the model trained on
real data only. We also investigate an effect of transfer learning by reusing
trained discriminator network for denoising model.
