February 28, 2020IEEE/ACM Transactions on Computational Biology and Bioinformatics139 citations

A New ECG Denoising Framework Using Generative Adversarial Network

Structured PICO

Does a CNN-based GAN model improve ECG denoising performance compared to traditional time domain signal decomposition methods?

Population

ECG signals from the MIT-BIH Arrhythmia database

Intervention

Convolutional neural network (CNN) based Generative Adversarial Network (GAN) model for ECG noise filtering

Comparator

Current ECG denoising techniques based on time domain signal decomposition methods (thresholding and filtering approaches)

Outcome

ECG denoising performance (qualitative and quantitative analyses)surrogate

A novel CNN-based GAN model provides improved end-to-end ECG denoising performance compared to traditional time-domain filtering methods.

Abstract

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.

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