Removal of baseline wander from ECG signal using cascaded Empirical Mode Decomposition and morphological functions

2016 ◽  
Author(s):  
M.S Choudhry ◽  
Akshay Puri ◽  
Rajiv Kapoor
Keyword(s):  
Empirical Mode Decomposition ◽  
Ecg Signal ◽  
Mode Decomposition ◽  
Baseline Wander

A New Strategy for ECG Baseline Wander Elimination Using Empirical Mode Decomposition

2016 ◽  
Vol 15 (02) ◽  
pp. 1650017 ◽  
Author(s):  
Mohammad Shahbakhti ◽  
Hamed Bagheri ◽  
Babak Shekarchi ◽  
Somayeh Mohammadi ◽  
Mohsen Naji
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Mode Decomposition ◽  
New Strategy ◽  
External Sources ◽  
Baseline Wander ◽  
Fifth Order

Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline noise from ECG is presented. When compared to other EMD-based methods, the novelty of this research is to reach the optimized number of decomposed levels for ECG BW de-noising using mean power frequency (MPF), while the reduction of processing time is considered. To evaluate the performance of the proposed method, a fifth-order Butterworth high pass filtering (BHPF) with cut-off frequency at 0.5[Formula: see text]Hz and wavelet approach are applied. Three performance indices, signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC), between pure and filtered signals have been utilized for qualification of presented techniques. Results suggest that the EMD-based method outperforms the other filtering method.

scholarly journals Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 11 (5) ◽  
pp. 7536-7541
Author(s):  
W. Mohguen ◽  
S. Bouguezel
Keyword(s):  
Empirical Mode Decomposition ◽  
Additive White Gaussian Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Ecg Signal ◽  
Mean Square ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Thresholding Function

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.

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