Deep Recurrent Neural Networks for ECG Signal Denoising

TL;DR

This paper introduces a deep recurrent neural network approach for ECG signal denoising, leveraging transfer learning with synthetic data to improve performance on real noisy signals, outperforming existing methods.

Contribution

It presents a novel deep recurrent neural network model that uses transfer learning from synthetic data to enhance ECG denoising performance on real signals.

Findings

Deep recurrent neural networks outperform reference methods on heavily noised signals.

Pretraining with synthetic data improves real signal denoising performance.

The proposed method achieves state-of-the-art results after fine-tuning.

Abstract

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.