scholarly journals An efficient ECG Denoising method using Discrete Wavelet with Savitzky-Golay filter

2019 ◽  
Vol 5 (1) ◽  
pp. 385-387 ◽  
Author(s):  
Fars Samann ◽  
Thomas Schanze
Keyword(s):  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Optimal Choice ◽  
Discrete Wavelet ◽  
Denoising Method ◽  
Frame Size ◽  
Polynomial Degree ◽  
Ecg Signals ◽  
Ecg Denoising ◽  
Mean Square Difference

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.

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

scholarly journals Cell-filtering-based multi-scale Shannon–Cosine wavelet denoising method for locust slice images

2019 ◽  
Vol 17 (05) ◽  
pp. 1950035 ◽  
Author(s):  
Shenghan Mei ◽  
Xiaochun Liu ◽  
Shuli Mei
Keyword(s):  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Wavelet Denoising ◽  
Experimental Results ◽  
Transform Method ◽  
Denoising Method ◽  
Multi Scale ◽  
Piecewise Smoothness

The locust slice images have all the features such as strong self-similarity, piecewise smoothness and nonlinear texture structure. Multi-scale interpolation operator is an effective tool to describe such structures, but it cannot overcome the influence of noise on images. Therefore, this research designed the Shannon–Cosine wavelet which possesses all the excellent properties such as interpolation, smoothness, compact support and normalization, then constructing multi-scale wavelet interpolative operator, the operator can be applied to decompose and reconstruct the images adaptively. Combining the operator with the local filter operator (mean and median), a multi-scale Shannon–Cosine wavelet denoising algorithm based on cell filtering is constructed in this research. The algorithm overcomes the disadvantages of multi-scale interpolation wavelet, which is only suitable for describing smooth signals, and realizes multi-scale noise reduction of locust slice images. The experimental results show that the proposed method can keep all kinds of texture structures in the slice image of locust. In the experiments, the locust slice images with mixture noise of Gaussian and salt–pepper are taken as examples to compare the performances of the proposed method and other typical denoising methods. The experimental results show that the Peak Signal-To-Noise Ratio (PSNR) of the denoised images obtained by the proposed method is greater 27.3%, 24.6%, 2.94%, 22.9% than Weiner filter, wavelet transform method, median and average filtering, respectively; and the Structural Similarity Index (SSIM) for measuring image quality is greater 31.1%, 31.3%, 15.5%, 10.2% than other four methods, respectively. As the variance of Gaussian white noise increases from 0.02 to 0.1, the values of PSNR and SSIM obtained by the proposed method only decrease by 11.94% and 13.33%, respectively, which are much less than other 4 methods. This shows that the proposed method possesses stronger adaptability.

scholarly journals Sparse ECG Denoising with Generalized Minimax Concave Penalty

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1718 ◽  
Author(s):  
Zhongyi Jin ◽  
Anming Dong ◽  
Minglei Shu ◽  
Yinglong Wang
Keyword(s):  
Root Mean Square ◽  
Signal To Noise Ratio ◽  
High Amplitude ◽  
Mean Square ◽  
Ecg Signals ◽  
Reduction Techniques ◽  
Ecg Denoising ◽  
Important Diagnostic Tool ◽  
Sparsity Recovery ◽  
Norm Penalty

The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional ℓ 1 -norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. Compared with the ℓ 1 -norm penalty, the non-differentiable non-convex GMC penalty has the potential to strongly promote sparsity while maintaining the convexity of the cost function. Moreover, the GMC punishes large values less severely than ℓ 1 -norm, which is utilized to overcome the drawback of underestimating the high-amplitude components for the ℓ 1 -norm penalty. The proposed methods are evaluated on ECG signals from the MIT-BIH Arrhythmia database. The results show that underestimating problem is overcome by the proposed GMC-based method. The GMC-based method shows significant improvement with respect to the average of output signal-to-noise ratio improvement ( S N R i m p ), the average of root mean square error (RMSE) and the percent root mean square difference (PRD) over almost any given SNR compared with the classical methods, thus providing promising approaches for ECG denoising.

scholarly journals Denoising of Heavily Contaminated Partial Discharge signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6540
Author(s):  
Mohammed A. Shams ◽  
Hussein I. Anis ◽  
Mohammed El-Shahat
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Voltage ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Gaussian White Noise ◽  
Discrete Wavelet ◽  
Power Cable ◽  
Power Cables ◽  
Algorithm Performance

Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

Noise reduction of shot-noise-dominated hyperspectral imagery by combining PCA with existing denoising methods

2021 ◽  
pp. 2150028
Author(s):  
Guang Yi Chen ◽  
Adam Krzyzak
Keyword(s):  
White Noise ◽  
Shot Noise ◽  
Signal To Noise Ratio ◽  
Hyperspectral Imagery ◽  
Gaussian White Noise ◽  
Principal Component ◽  
Data Cube ◽  
Wavelet Shrinkage ◽  
Denoising Method ◽  
Imagery Data

In this paper, we revisit the effects of principal component analysis (PCA) on hyperspectral imagery denoising. Our previous work combined PCA with wavelet shrinkage and particularly good denoising results has been achieved. We debate that any denoising methods can be used to replace wavelet shrinkage in our PCA+wavelet shrinkage algorithm. The major difference between this work and our previous PCA-based denoising method is that we consider a mixture of Gaussian and shot noise in this work whereas our previous methods studied Gaussian white noise alone. In addition, we retain [Formula: see text] [Formula: see text] PCA output components in our forward PCA transform in this paper whereas we keep all PCA output components [Formula: see text] in our previous works. The [Formula: see text] above is the number of spectral bands in the original hyperspectral imagery data cube. In addition, PCA is much better than nonlinear PCA for hyperspectral imagery denoising when Gaussian white noise and shot noise are introduced as demonstrated in this paper. Extensive experiments demonstrate that the method proposed in this paper outperforms the existing methods significantly in terms of signal-to-noise ratio for two testing hyperspectral imagery data cubes.

Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

2016 ◽  
Vol 6 (5) ◽  
pp. 2197 ◽  
Author(s):  
Amy Hamidah Salman ◽  
Nur Ahmadi ◽  
Richard Mengko ◽  
Armein Z. R. Langi ◽  
Tati L. R. Mengko
Keyword(s):  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Sound Signal ◽  
Mean Square ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mean Square Difference ◽  
Heart Sound Signal

<p>In this paper, a denoising method for heart sound signal based on empirical mode decomposition (EMD) is proposed. To evaluate the performance of the proposed method, extensive simulations are performed using synthetic normal and abnormal heart sound data corrupted with white, colored, exponential and alpha-stable noise under different SNR input values. The performance is evaluated in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD), and compared with wavelet transform (WT) and total variation (TV) denoising methods. The simulation results show that the proposed method outperforms two other methods in removing three types of noises.</p>

scholarly journals REMOVAL OF ELECTRODE MOTION ARTIFACT IN ECG SIGNALS USING WAVELET BASED THRESHOLD METHODS WITH GREY INCIDENCE DEGREE

2013 ◽  
pp. 11-18
Author(s):  
G. UMAMAHESWARA REDDY ◽  
M. MURALIDHAR
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Modern Society ◽  
Motion Artifact ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Grey Theory ◽  
Wavelet Thresholding ◽  
Threshold Method ◽  
Ecg Signals

Cardiovascular diseases are one of the most frequent and dangerous problems in modern society in nowadays. Unfortunately electrocardiograms (ECG) signals, during their acquisition process, are affected by various types of noise and artifacts due to the movement, or breathing of the patient, electrode contact, power-line interferences, etc. The aim of this study was to develop an algorithm to remove electrode motion artifact in ECG signals. Donoho and Johnstone proposed Wavelet thresholding de-noising method based on discrete wavelet transform (DWT) is suitable for non-stationary signals. The wavelet transform coefficient is processed by using grey relation analysis of the grey theory, and a new wavelet threshold method namely wavelet threshold method with grey incidence degree (GID) (or the GID threshold method) based is introduced. It shows that the signal smoothness and similarity of the two signal criteria have been greatly improved by the GID threshold method compared with existing threshold methods. According to the characteristics of different ECG signals, GID threshold method gets better results than it can adaptively deal with noise separation and details remaining of the two opposing signal problems, so as to provide a better choice for wavelet threshold methods of signal processing. Performance analysis was performed by evaluating Mean Square Error (MSE), Signal-to-noise ratio (SNR) and visual inspection over the denoised signal from each algorithm. The experimental result shows that GID hard shrinkage method with sub-band or level dependent thresholding gives the best denoising performance on ECG signal. The result shows that soft threshold not always gives better denoising performance; it depends on which wavelet thresholding algorithm was chosen.

scholarly journals Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters

2021 ◽  
Vol 12 (1) ◽  
pp. 01-15
Author(s):  
Rui Gong ◽  
Kazunori Hase ◽  
Hajime Ohtsu ◽  
Susumu Ota
Keyword(s):  
Ant Colony Optimization ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Ant Colony ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Snr Improvement ◽  
Mean Square Difference ◽  
Dynamic Filter

This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals.

Image Denoising Using Wavelet Transform and Wavelet Transform with Enhanced Diversity

2011 ◽  
Vol 403-408 ◽  
pp. 866-870
Author(s):  
Vaibhav Nigam ◽  
Smriti Bhatnagar ◽  
Sajal Luthra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Practical Implementation ◽  
Discrete Wavelet ◽  
Maximum Output ◽  
Denoising Method ◽  
Detail Coefficient

This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by computing wavelet transforms with different parameters. After the filtering of each detail coefficient, the corresponding wavelet transforms are inverted and the estimated image, having a higher PSNR, is extracted. To benchmark against the best possible denoising method three thresholding techniques have been compared. In this paper we have presented a more practical, implementation oriented work.

scholarly journals An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

2019 ◽  
Vol 9 (22) ◽  
pp. 4968 ◽  
Author(s):  
Dengyong Zhang ◽  
Shanshan Wang ◽  
Feng Li ◽  
Jin Wang ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Cardiac Disease ◽  
Signal To Noise Ratio ◽  
Signal Quality ◽  
Ecg Signal ◽  
Mean Square ◽  
Wavelet Coefficients ◽  
Ecg Signals ◽  
Smoothing Filter ◽  
Wavelet Energy ◽  
Mean Square Difference

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

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