Removal of Noise from ECG Signals using Residual Generative Adversarial Network
Mohebbanaaz Mohebbanaaz, Y. Padma Sai, L. V. Rajani Kumari
Abstract
Removal of Noise from ECG has a great significance in diagnosis of cardiac diseases. Denoising is the foremost step in ECG signal pre-processing tasks. The existing denoising methods in the literature does not provide any linear relationship between signals and does not adaptively work for various types of noises. In this study, a Residual Generative Adversarial Network (R-GAN) structure is proposed for ECG noise filtering. R-GAN has a generator and discriminator unit. The generator network is designed using encoder, residual block, decoder and discriminator is designed using Convolutional layers. Signals are collected from MIT-BIH database for quantitative and qualitative analysis. Various denoising methods in the literature have been explored to make a fair comparison. Experimental results shows that our proposed methodology can effectively retain significant information carried by the given ECG signal compared to existing state of art methods.