Baseline wander and Power line interference elimination from Cardiac signals using Error Nonlinearity LMS algorithm

Summary An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise‐cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well‐known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step‐size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post‐signal‐to‐noise ratio, mean square error, and percentage root mean square difference.

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“…In the past few years, various methods have been used to denoise ECG signals . Adaptive filters are an example of one of these methods.…”

Section: Introductionmentioning

confidence: 99%

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

Matrawy

Khalaf

2020

Adaptive Control & Signal

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“…Low SNR can difficult the analysis performed by experts or computational applications, since it changes the signal waveform. Typical noise present in ECG signals are due to power-line interference in a frequency band varying from 50 Hz to 60 Hz (Łęski and Henzel, 2005;Patil and Chavan, 2012;Rahman et al, 2010) depending on the country. It occurs due to interferences of electrical equipment as X-ray, air conditioners, elevators (Patil and Chavan, 2012), and also due to the differences in electrode impedances (Bahoura and Ezzaidi, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Some noise reduction techniques are based on digital filters, wavelet transform and adaptive filtering (AlMahamdy and Riley, 2014); singular value decomposition (Bandarabadi and Karami-Mollaei, 2010); independent component analysis (Phegade and Mukherji, 2013) and S-transform (Das and Ari, 2013). Among the algorithms for PLI removal there are digital processing methods based on: fuzzy thresholding (Üstündağ et al, 2012); nonlinear filter bank (Łęski and Henzel, 2005); Fast Fourier Transform and adaptive nonlinear noise estimator (Shirbani and Setarehdan, 2013); Empirical Mode Decomposition (Agrawal and Gupta, 2013); neural networks (Mateo et al, 2008) and wavelet transform (Agrawal and Gupta, 2013;Garg et al, 2011;Poornachandra and Kumaravel, 2008;Rahman et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet analysis has been successfully used for ECG signal denoising because it deals well with non-stationary signals and also presents better resolution in time-frequency domain than Fourier analysis (Rahman et al, 2010). In the comparative study presented by AlMahamdy and Riley (2014), the wavelet transform produced better results in most of the experiments.…”

Section: Introductionmentioning

confidence: 99%

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A wavelet-based method for power-line interference removal in ECG signals

et al. 2018

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Introduction: The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques. Methods: This method consists in identifying the ECG and noise frequency range for further zeroing wavelet detail coefficients in the subbands with no ECG coefficients in the frequency content. Afterward, the enhanced ECG signal is obtained by the inverse discrete wavelet transform (IDWT

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“…Iliev, et al [13] proposed a composite filter to delete both powerline interference and baseline wandering. Rahman, et al [14] removed the artifacts by applying adaptive filter based on error nonlinearity Least Mean Square (LMS) algorithm. Furthermore, the Constrained Stability Least Mean Square (CSLMS) algorithm [15] was applied to decrease mean-square error of LMS algorithm.…”

Section: Introductionmentioning

confidence: 99%

Removal of baseline wandering in ECG signal by improved detrending method

Shin

Kim

Song

et al. 2015

BME

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Abstract. The very first step to process electrocardiogram (ECG) signal is to eliminate baseline wandering interference that is usually caused by electrode-skin impedance mismatch, motion artifacts due to a patient's body moment or respiratory breathing. A new method is thus suggested to remove baseline wandering in ECG by improving the detrending method that was originally proposed for eliminating slow non-stationary trends from heart rate variability (HRV). In our proposed method, a global trend is estimated in terms of baseline wandering by merging the local trend based on an ECG segment that represents a part of the ECG signal. The experimental results show that the improved detrending method can efficiently resolve baseline wandering without distorting any morphological characteristic embedded in the ECG signal in no time delay manner.

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Baseline wander and Power line interference elimination from Cardiac signals using Error Nonlinearity LMS algorithm

Cited by 11 publications

References 22 publications

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

A wavelet-based method for power-line interference removal in ECG signals

Removal of baseline wandering in ECG signal by improved detrending method

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