Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition
Received: 18 June 2021 | Revised: 4 July 2021 and 18 July 2021 | Accepted: 1 August 2021 | Online: 23 August 2021
Corresponding author: W. Mohguen
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.
Keywords:
denoising, ECG, EMD, EEMD, customized thresholdingDownloads
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