ECG Signal Denoising and Reconstruction Based on Basis Pursuit

Liu, Ruixia; Shu, Minglei; Chen, Changfang

doi:10.3390/app11041591

Cited by 22 publications

(13 citation statements)

References 45 publications

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“…Traditional filtering based on finite impulse response (FIR) filters was proposed by Van Alste and Schilder (1985) [5]. To remove electrical noises from the ECG signal, least squaresbased adaptive filtering is utilized in [6]. An adaptive Kalman filter is utilized to enhance the ECG signal quality in the same scenario.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

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Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Balasubramanian

Tewari

Naruk³

2022

IJRITCC

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Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this paper, the noisy ECG signal is denoised based on Discrete Wavelet Transform (DWT) optimized with the Enhanced African Vulture Optimization (AVO) algorithm and adaptive switching mean filter (ASMF) is proposed. Initially, the input ECG signals are obtained from the MIT-BIH ARR dataset and white Gaussian noise is added to the obtained ECG signals. Then the corrupted ECG signals are denoised using Discrete Wavelet Transform (DWT) in which the threshold is optimized with an Enhanced African Vulture Optimization (AVO) algorithm to obtain the optimum threshold. The AVO algorithm is enhanced by Whale Optimization Algorithm (WOA). Additionally, ASMF is tuned by the Enhanced AVO algorithm. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the EAVO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of SNR, mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

“…If the variance among the ECG sample and windowed area's mean value is more than the threshold limit, the sample is considered corrupted, and its value is adjusted to match that of the mean. The ASMF operation's mathematical formulation is expressed in Equation (6).…”

Section: B Adaptive Switching Mean Filtermentioning

confidence: 99%

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Balasubramanian

Tewari

Naruk³

2022

IJRITCC

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“…Different sparsity-based algorithms have been developed in the past to de-noise and recover sparse signals and images, that is, soft thresholding [1], hard thresholding [2], [3], [4], firm thresholding [5], non-negative garrote thresholding [6], hyperbolic tangent thresholding [7], logarithmic thresholding [8], hankel sparse low-rank approximation [9], proximal operators [10], [11], [12], alternating direction method of multipliers [13], [14], block thresholding [15], and overlapping group shrinkage (OGS) [16]. Along with these established techniques, some new techniques are also used for de-noising of specific image types.…”

Section: Introductionmentioning

confidence: 99%

“…If the transform is orthogonal, then the noise in the transform domain has the same correlation function as the original noise in the signal domain, therefore, when the transform is orthogonal, white noise in the signal domain becomes white noise in the transform domain. To approximate a suitable r (the estimated signal), basis pursuit de-noising [13], [20] and LASSO (least absolute shrinkage and selection operator) [21] are used in literature, which result in a soft threshold function.This function is frequently used for the de-noising and recovery of sparse signals. Chen previously used OGS for de-noising of group sparse signals [16].…”

Section: Introductionmentioning

confidence: 99%

De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

et al. 2023

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In this work a new thresholding function referred to as 'mixture model shrinkage' (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.INDEX TERMS Convex optimization, EM algorithm, maximum a posteriori estimator (MAP), MM algorithm, PSNR, RMSE, sparse signal processing.

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“…Currently, commonly used compression and re-construction algorithms include greedy algorithm [ 10 ], convex optimization algorithm [ 11 ], Bayesian learning [ 12 ], etc. For example, Liu et al [ 13 ] used a low-pass filtering method to optimize the electrographic signal and used basis pursuit (BP) algorithm to compress and reconstruct the electrocardiogram signal. Cheng et al [ 14 ] used an improved orthogonal matching pursuit (OMP) algorithm to improve seismic data’s reconstruction speed and compression effect.…”

Section: Introductionmentioning

confidence: 99%

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Bai

Wen

et al. 2022

Sensors

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It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method’s essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.

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ECG Signal Denoising and Reconstruction Based on Basis Pursuit

Cited by 22 publications

References 45 publications

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

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