2018/07/30 by Karol Antczak, Antczak, Karol · 3 citations
Engineering · Medicine · Neuroscience · #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Neural and Evolutionary Computing (cs.NE) #Non-Invasive Vital Sign Monitoring #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1807.11551
openalex publication_date 2018/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.