An ECG Denoising Method Based on Hybrid MLTP-EEMD Model

AbstractElectrocardiogram (ECG) is a widely used tool for the early diagnosis and evaluation of cardiac disorders. The ECG signal is usually distorted during recording by different types of noise which may lead to incorrect diagnosis. Therefore, clear ECG signals are required to support good cardiac disorder diagnosing. In this paper, an efficient ECG denoising method using combined discrete wavelet with Savitzky-Golay (S-G) filter is proposed. The performance of S-G filter is studied in terms of polynomial degree and frame size, i.e. signal section. In addition, the performance of denoising wavelet is studied in term of mother wavelet type and wavelet order. The advantage of S-G filter is combined with discrete wavelet denoising method to get better denoising performance. The performance of denoising ECG are evaluated using signal to noise ratio (SNR) and percentage root mean square difference (PRD). For this we used simulated and gaussian white noise surrogated ECG signals. Our results show that combined S-G and wavelet filter denoising is noticeable better than the respective individual procedures. In addition, we found that the selection of frame size, order of the S-G filter and the wavelet type and order should be done carefully in order to get optimal results. It also holds true for the new filter that the optimal choice of filter parameters is a compromise between noise reduction and distortion.

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An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

Wireless Communications and Mobile Computing ◽

10.1155/2020/8811962 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Dengyong Zhang ◽

Shanshan Wang ◽

Feng Li ◽

Shang Tian ◽

Jin Wang ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Sample Entropy ◽

Threshold Function ◽

Signal Denoising ◽

Ecg Signal ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Ecg Signals ◽

Mode Decomposition ◽

Ecg Denoising

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

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A Novel Technique of ECG Denoising based on Lifting Wavelet Transform and Total Variation Minimization

10.21203/rs.3.rs-30285/v1 ◽

2020 ◽

Author(s):

Talbi Mourad ◽

Baazaoui Riadh

Keyword(s):

Wavelet Transform ◽

Total Variation ◽

Wavelet Coefficient ◽

Additive White Gaussian Noise ◽

The Other ◽

Lifting Wavelet Transform ◽

Denoising Method ◽

Total Variation Minimization ◽

Ecg Denoising ◽

Lifting Wavelet

Abstract Buckground:The signal of Electrocardiogram (•) is one of the most popular diagnostic means providing an electrical picture of the heart and also information about different pathological conditions. Due to the path deformities and external electrical disturbances, the signal of becomes noisy. Hence, in literature, many ECG denoising algorithms have been proposed and among them we can mention the techniques based on wavelet coefficient shrinkage. The purpose of this paper is to denoise ECG signals applying a new ECG Denoising technique and proving its performance compared to some denoising approaches existing in literature. This new proposed technique of B Denoising consists at the first step in applying the Lifting Wavelet Transform (u) to the noisy c Signal (where 2 is the decomposition level) in order to obtain three noisy wavelet sub-bands, k, g and r. The two coefficients, o, u are details ones and they are denoised by soft or hard thresholding in order to obtain denoised coefficients, n1 and d2. The coefficient : is an approximation one and is denoised by Total Variation Minimization in order to obtain a denoised one, T2. Finally, the inverse of his applied to e, and s in order to obtain the denoised i signal. The evaluation of this proposed technique is performed by comparing it to three other denoising approaches existing in literature. The first one of these approaches is based on g, the second one is n double-density complex a denoising method and the third one is based on non local means.Results:All These techniques are applied on a number of lsignals taken from database and corrupted by an additive White Gaussian noise at different values of Signal to Noise Ratio (o). The obtained results from the computation of the f and the Mean Square Error ( ), show that the proposed technique outperforms the other three mentioned techniques.Conclusion:In this paper, the proposed ECG denoising technique based on E and l, outperforms the other previously mentioned denoising approaches and this based on the computation of the SNR and MSE.

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