A New ECG Denoising Framework Using Generative Adversarial Network

This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks. The current ECG denoising techniques are based on the time domain signal decomposition methods. These methods use some kind of thresholding and filtering approaches. In our proposed technique, convolutional neural network (CNN) based GAN model is effectively trained for ECG noise filtering. In contrast to existing techniques, we performed end-to-end GAN model training using the clean and noisy ECG signals. MIT-BIH Arrhythmia database is used for all the qualitative and quantitative analyses. The improved ECG denoising performance open the door for further exploration of GAN based ECG denoising approach.

Automated summary

This paper introduces a novel ECG denoising approach based on generative adversarial network, using convolutional neural network for noise filtering. The improved denoising performance opens up further exploration of GAN based ECG denoising methods.