Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition

W Mohguen,S Bouguezel

doi:10.48084/etasr.4302

Abstract

In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.

Highlights

Empirical Mode Decomposition (EMD) is a powerful algorithm for splitting non-stationary signals [1]
Since the useful information of the signal is often concentrated on lowfrequency Intrinsic Mode Functions (IMFs) and the noise is primarily located in high-frequency IMFs, another approach is to perform denoising by partial construction of the signal with the IMFs that contain useful information [2, 14]
The proposed Ensemble Empirical Mode Decomposition (EEMD)-Custom algorithm was applied to 8 real biomedical ECG signals using the MIT-BIH database [20], labeled 111m, 112m, 113m, 114m, 115m, 116m, 121m, and 122m

Summary

Introduction

Empirical Mode Decomposition (EMD) is a powerful algorithm for splitting non-stationary signals [1]. EMD techniques have been used for signal denoising, and those based on thresholding were developed in [210]. A denoising technique can be based on signal estimation using all the previously thresholded IMFs [3,4,5,6,7,8,9,10,11,12,13]. Authors in [2] proposed a method for estimating the energy of noisy IMFs from a theoretical model and IMFs' energies of the test signal, and the signal was reconstructed partially by using only the IMFs that contained useful information, eliminating those that essentially maintained noise. To overcome the drawbacks of EMD such as mode mixing (presence of oscillations of different amplitudes in one mode) [1], a variant of the EMD algorithm called Ensemble Empirical Mode Decomposition (EEMD) was proposed in [19]. EEMD achieved better denoising performance than EMD with a reduced number of trials

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Conclusion