Hyper-trim shrinkage for denoising of ECG signal

Poornachandra, S.; Kumaravel, N.

doi:10.1016/j.dsp.2004.12.005

Cited by 48 publications

(25 citation statements)

References 5 publications

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“…The SNR improvements obtained from our hybrid denoising technique are compared to other reported noise elimination techniques (for the same datasets); wavelet based techniques [1,7,8], nonlinear Bayesian Filter [23], low-pass filter and adaptive filtering approach [10]. These techniques are applied to the same database.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 5 shows the SNR improvement for the previous techniques (which are applied to our dataset) compared with the SNR improvement obtained by the proposed hybrid denoising technique. So it can be seen from the results that the hybridization of GA and wavelet trans- The improvement SNR for several common denoising techniques applied to our dataset compared with the SNR improvement obtained by the proposed hybrid denoising technique: (A) Wavelet based denoising [7,8]. (B) Low-pass filter [10].…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, by setting the wavelet coefficients that corresponding to these small details to zero, the noise vanishes. This becomes the basic concept behind thresholding: applying the IDWT on the results may lead to reconstruction with essential signal characteristics and less noise [7,8]. Two types of thresholding functions are often used: Hard thresholding and Soft thresholding [5,6].…”

Section: Wavelet Denoising Principlementioning

confidence: 99%

“…Methods based on shrinkage of wavelet coefficients are very popular for estimation of biological signals. Most ECG signal wavelet denoising algorithms are based on Donoho's Universal theory [1,4,[7][8][9]. Sayadi et al [1] proposed a technique to remove noise from the ECG using adaptive wavelet transform, named bionic wavelet transform.…”

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confidence: 99%

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Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

El‐Dahshan

2010

Telecommun Syst

100

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This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Wavelet Denoising Principlementioning

confidence: 99%

mentioning

confidence: 99%

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Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

El‐Dahshan

2010

Telecommun Syst

100

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“…Poornachandra and N. Kumaravel suggested hyper trim shrinkage [5]. Hyper trim shrinkage is an enhanced thresholding function based on universal threshold [1].…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of various wavelet shrinkage functions for ECG signals

Selvakumarasamy

Chandra²,

Amutha³

2014

2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)

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ECG is the generally recognized biomedical signal for diagnostic purpose and it shows the electrical activity of the heart muscles. ECG is a non stationary signal where wavelet transform (WT) is useful for analyzing it. Denoising is the process of removing noise in order to preserve useful information. A comparative analysis of various wavelet shrinkage functions for ECG signal denoising such as soft and hard shrinkage functions, hyper shrinkage function, subband -adaptive shrinkage functions are discussed. The performance of the wavelet shrinkage functions are compared based on the signal to noise ratio (SNR) Value.

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Class-Adaptive Denoising for EEG Data Classification

Martišius

Damaševičius

2012

Artificial Intelligence and Soft Computing

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Hyper-trim shrinkage for denoising of ECG signal

Cited by 48 publications

References 5 publications

Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

A comparative analysis of various wavelet shrinkage functions for ECG signals

Class-Adaptive Denoising for EEG Data Classification

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