  Electrocardiographic signal is a subject to multiple noises, caused by
various factors. It is therefore a standard practice to denoise such signal
before further analysis. With advances of new branch of machine learning,
called deep learning, new methods are available that promises state-of-the-art
performance for this task. We present a novel approach to denoise
electrocardiographic signals with deep recurrent denoising neural networks. We
utilize a transfer learning technique by pretraining the network using
synthetic data, generated by a dynamic ECG model, and fine-tuning it with a
real data. We also investigate the impact of the synthetic training data on the
network performance on real signals. The proposed method was tested on a real
dataset with varying amount of noise. The results indicate that four-layer deep
recurrent neural network can outperform reference methods for heavily noised
signal. Moreover, networks pretrained with synthetic data seem to have better
results than network trained with real data only. We show that it is possible
to create state-of-the art denoising neural network that, pretrained on
artificial data, can perform exceptionally well on real ECG signals after
proper fine-tuning.
