Adaptive Wavelet Wiener Filtering of ECG Signals

doi:10.1109/TBME.2012.2228482

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Adaptive Wavelet Wiener Filtering of ECG Signals

Journal Article•DOI•

Adaptive Wavelet Wiener Filtering of ECG Signals

Lukas Smital¹, Martin Vitek¹, Jiri Kozumplik¹, Ivo Provaznik¹•Institutions (1)

Brno University of Technology¹

01 Feb 2013-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 60, Iss: 2, pp 437-445

TL;DR: This study focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation and used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise- free signal.

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Abstract: In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.

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Journal Article•DOI•

An Efficient Threshold Prediction Scheme for Wavelet Based ECG Signal Noise Reduction Using Variable Step Size Firefly Algorithm

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Vinu Sundararaj

30 Sep 2016-International Journal of Intelligent Engineering and Systems

TL;DR: An optimized threshold mechanism is proposed for wavelet based medical signal noise reduction based on a variable step size firefly algorithm (VSSFA) in dual tree complex wavelet scheme, in which the VSSFA is utilized for threshold optimization.

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Abstract: Electrocardiographic (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy Electrocardiographic (ECG) signal is very motivating challenge. Prior to automated analysis, the noises present in ECG signal need to be eliminated for accurate diagnosis. Many researchers have been reported different methods for denoising the ECG signal in recent years. In this paper, an optimized threshold mechanism is proposed for wavelet based medical signal noise reduction. This scheme is based on a variable step size firefly algorithm (VSSFA) in dual tree complex wavelet scheme, in which the VSSFA is utilized for threshold optimization. This approach is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5dB and 10dB. Simulation result illustrate that the proposed system is well performance in various noise level, and obtains better visual quality compare with other methods.

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273 citations

Journal Article•DOI•

ECG signal enhancement based on improved denoising auto-encoder

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Peng Xiong¹, Hongrui Wang², Ming Liu², Suiping Zhou³, Zeng-Guang Hou⁴, Xiuling Liu² - Show less +2 more•Institutions (4)

Yanshan University¹, Hebei University², Middlesex University³, Chinese Academy of Sciences⁴

01 Jun 2016-Engineering Applications of Artificial Intelligence

TL;DR: The proposed deep neural network (DNN) is created from an improved denoising auto-encoder reformed by a wavelet transform (WT) method, which showed significant improvement in SNR and RMSE compared with the individual processing with either a WT or DAE, thus providing promising approaches for ECG signal enhancement.

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113 citations

Cites methods from "Adaptive Wavelet Wiener Filtering o..."

...…(EMD) (Karagiannis and Constantinou, 2011; Kabir and Shahnaz, 2012), adaptive filtering (Sajjad et al., 2012; Rahman et al., 2012; Moradi et al., 2014), and the wavelet method (Awal et al., 2012, 2014; KapilTajane and Pitale, 2014; Reddy et al., 2009; Gokhale, 2012; Smital et al., 2013)....
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...In the work of Smital et al. (2013), the dyadic stationary wavelet transform in the Wiener filter was used to estimate the noise-free signal....
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Journal Article•DOI•

Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications

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Wanqing Wu¹, Sandeep Pirbhulal¹, Arun Kumar Sangaiah², Subhas Chandra Mukhopadhyay³, Guanglin Li¹ - Show less +1 more•Institutions (3)

Chinese Academy of Sciences¹, VIT University², Macquarie University³

01 Sep 2018-Future Generation Computer Systems

TL;DR: Developed textile electrode with knitted structure and conductive material comprising cotton /nylon fiber coated silver is investigated and can be useful for future research to offer the balance for SQC ratio for ECG measurement in fog computing based healthcare systems.

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112 citations

Journal Article•DOI•

Parallel-type fractional zero-phase filtering for ECG signal denoising

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Jianhong Wang¹, Jianhong Wang², Yongqiang Ye², Xiang Pan², Xudong Gao² - Show less +1 more•Institutions (2)

Nantong University¹, Nanjing University of Aeronautics and Astronautics²

01 Apr 2015-Biomedical Signal Processing and Control

TL;DR: The method presented eliminates the phase distortion while offering a better compromise between signal denoising and signal information retention than conventional filtering methods.

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82 citations

Journal Article•DOI•

Fractional zero-phase filtering based on the Riemann-Liouville integral

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Jianhong Wang¹, Jianhong Wang², Yongqiang Ye², Xiang Pan², Xudong Gao², Zhuang Chao² - Show less +2 more•Institutions (2)

Nantong University¹, Nanjing University of Aeronautics and Astronautics²

01 May 2014-Signal Processing

TL;DR: Two novel and computationally efficient, zero-phase filtering techniques are proposed based on the Riemann-Liouville integral that better enhance the compromise capability between signal denoising and signal information retention than the conventional filtering methods do.

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64 citations

Cites background or methods from "Adaptive Wavelet Wiener Filtering o..."

...The proposed methods are evaluated on Electrocardiogram (ECG) signal, by adding disturbance, random, and white Gaussian noises to visually clean ECG record, and studying SNR and MSE of the filter outputs....
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...From the viewpoint of energy, SNR can be seen as the ratio of signal power to the noise power....
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...3 present the comparison of SNR and MSE using zero-phase filtering with different orders....
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...SNR and MSE are expressed as SNR¼ 10 log10 ∑ Np n ¼ 1 xðnÞ2 ∑ Np n ¼ 1 ðyðnÞ xðnÞÞ2 #," ð21Þ and MSE¼ 1 Np ∑ Np n ¼ 1 ðyðnÞ xðnÞÞ2 ð22Þ respectively, where Np is the sampling points, x(n) is the original input signal, and y(n) is the output signal of digital filters....
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...Nevertheless, in this sample, the maximum SNR and minimum MSE can be achieved by setting ν¼ 0:8....
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References

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A wavelet tour of signal processing

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Stéphane Mallat

01 Jan 1998

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.

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Abstract: Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

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17,693 citations

"Adaptive Wavelet Wiener Filtering o..." refers background or methods in this paper

...It is a robust estimate of the standard deviation of noise using the median; it was first introduced in [12] and used, for example, in [15], [17] and [18],...
[...]
...They include, for example, the Universal threshold [12], SURE threshold (Stein’s unbiased risk estimate) [13], [14] or Minimax threshold [15]....
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Journal Article•DOI•

Ideal spatial adaptation by wavelet shrinkage

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David L. Donoho¹, Jain M. Johnstone¹•Institutions (1)

Stanford University¹

01 Sep 1994-Biometrika

TL;DR: In this article, the authors developed a spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients, and achieved a performance within a factor log 2 n of the ideal performance of piecewise polynomial and variable-knot spline methods.

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Abstract: SUMMARY With ideal spatial adaptation, an oracle furnishes information about how best to adapt a spatially variable estimator, whether piecewise constant, piecewise polynomial, variable knot spline, or variable bandwidth kernel, to the unknown function. Estimation with the aid of an oracle offers dramatic advantages over traditional linear estimation by nonadaptive kernels; however, it is a priori unclear whether such performance can be obtained by a procedure relying on the data alone. We describe a new principle for spatially-adaptive estimation: selective wavelet reconstruction. We show that variable-knot spline fits and piecewise-polynomial fits, when equipped with an oracle to select the knots, are not dramatically more powerful than selective wavelet reconstruction with an oracle. We develop a practical spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients. RiskShrink mimics the performance of an oracle for selective wavelet reconstruction as well as it is possible to do so. A new inequality in multivariate normal decision theory which we call the oracle inequality shows that attained performance differs from ideal performance by at most a factor of approximately 2 log n, where n is the sample size. Moreover no estimator can give a better guarantee than this. Within the class of spatially adaptive procedures, RiskShrink is essentially optimal. Relying only on the data, it comes within a factor log 2 n of the performance of piecewise polynomial and variableknot spline methods equipped with an oracle. In contrast, it is unknown how or if piecewise polynomial methods could be made to function this well when denied access to an oracle and forced to rely on data alone.

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8,153 citations

"Adaptive Wavelet Wiener Filtering o..." refers background or methods in this paper

...versal threshold [12], Stein’s unbiased risk estimate threshold (SURE) [13], [14], or Minimax threshold [15]....
[...]
...2) Thresholding Method: In this paper, we tested five thresholding algorithms: hard and soft thresholding [12], hyperbolic [21], nonnegative garrote [22], and semisoft (firm) thresholding [23], [24]....
[...]
...It is a robust estimate of the standard deviation of noise using the median; it was first introduced in [12] and used, for example, in [15], [17] and [18]...
[...]

Journal Article•DOI•

Adapting to Unknown Smoothness via Wavelet Shrinkage

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David L. Donoho¹, Iain M. Johnstone¹•Institutions (1)

Stanford University¹

01 Dec 1995-Journal of the American Statistical Association

TL;DR: In this article, the authors proposed a smoothness adaptive thresholding procedure, called SureShrink, which is adaptive to the Stein unbiased estimate of risk (sure) for threshold estimates and is near minimax simultaneously over a whole interval of the Besov scale; the size of this interval depends on the choice of mother wavelet.

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Abstract: We attempt to recover a function of unknown smoothness from noisy sampled data. We introduce a procedure, SureShrink, that suppresses noise by thresholding the empirical wavelet coefficients. The thresholding is adaptive: A threshold level is assigned to each dyadic resolution level by the principle of minimizing the Stein unbiased estimate of risk (Sure) for threshold estimates. The computational effort of the overall procedure is order N · log(N) as a function of the sample size N. SureShrink is smoothness adaptive: If the unknown function contains jumps, then the reconstruction (essentially) does also; if the unknown function has a smooth piece, then the reconstruction is (essentially) as smooth as the mother wavelet will allow. The procedure is in a sense optimally smoothness adaptive: It is near minimax simultaneously over a whole interval of the Besov scale; the size of this interval depends on the choice of mother wavelet. We know from a previous paper by the authors that traditional smoot...

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4,699 citations

"Adaptive Wavelet Wiener Filtering o..." refers background in this paper

...versal threshold [12], Stein’s unbiased risk estimate threshold (SURE) [13], [14], or Minimax threshold [15]....
[...]

Journal Article•DOI•

Estimation of the Mean of a Multivariate Normal Distribution

[...]

Charles Stein

01 Nov 1981-Annals of Statistics

TL;DR: In this article, an unbiased estimate of risk is obtained for an arbitrary estimate, and certain special classes of estimates are then discussed, such as smoothing by using moving averages and trimmed analogs of the James-Stein estimate.

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Abstract: Estimation of the means of independent normal random variables is considered, using sum of squared errors as loss. An unbiased estimate of risk is obtained for an arbitrary estimate, and certain special classes of estimates are then discussed. The results are applied to smoothing by use of moving averages and to trimmed analogs of the James-Stein estimate. A suggestion is made for calculating approximate confidence sets for the mean vector centered at an arbitrary estimate.

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2,866 citations

"Adaptive Wavelet Wiener Filtering o..." refers background in this paper

...versal threshold [12], Stein’s unbiased risk estimate threshold (SURE) [13], [14], or Minimax threshold [15]....
[...]
...They include, for example, the Universal threshold [12], Stein’s unbiased risk estimate threshold (SURE) [13], [14], or Minimax threshold [15]....
[...]

Journal Article•DOI•

Better subset regression using the nonnegative garrote

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Leo Breiman¹•Institutions (1)

University of California, Berkeley¹

30 Nov 1995-Technometrics

TL;DR: In this paper, a new method called nonnegative garrote (NN) was proposed for doing subset regression, which both shrinks and zerosizes coefficients and produces lower prediction error than ordinary subset selection.

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Abstract: A new method, called the nonnegative (nn) garrote, is proposed for doing subset regression. It both shrinks and zeroes coefficients. In tests on real and simulated data, it produces lower prediction error than ordinary subset selection. It is also compared to ridge regression. If the regression equations generated by a procedure do not change drastically with small changes in the data, the procedure is called stable. Subset selection is unstable, ridge is very stable, and the nn-garrote is intermediate. Simulation results illustrate the effects of instability on prediction error.

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1,026 citations

"Adaptive Wavelet Wiener Filtering o..." refers methods in this paper

...2) Thresholding Method: In this paper, we tested five thresholding algorithms: hard and soft thresholding [12], hyperbolic [21], nonnegative garrote [22], and semisoft (firm) thresholding [23], [24]....
[...]