Generative Adversarial Networks can synthesize realistic ECG signals that improve the training and performance of denoising autoencoders.
May augment ECG denoising training; leaves open clinical utility pending prospective validation.
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.
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Karol Antczak (2020) studied this question.
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