Electrocardiogram (ECG) signal modeling and noise reduction using wavelet neural networks

Poungponsri, Suranai; Yu, Xiaohua

doi:10.1109/ical.2009.5262892

Cited by 22 publications

(6 citation statements)

References 5 publications

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“…Methods for fECG SNR improvement are described in [ 87 ] where a Functional Link Artificial Neural Network (FLANN) is proposed to remove the Gaussian and baseline wander noise. Zhang and Benveniste [ 88 ] and Poungponsri and Yu [ 89 ] use NN combined with Wavelet transform for better results. However, in a recent article Poungponsri and Yu [ 51 ] come with an improvement of the method in [ 89 ] and the algorithm is tested also on PLI cancellation (Wavelet Neural Network—WNN).…”

Section: Methodsmentioning

confidence: 99%

“…Zhang and Benveniste [ 88 ] and Poungponsri and Yu [ 89 ] use NN combined with Wavelet transform for better results. However, in a recent article Poungponsri and Yu [ 51 ] come with an improvement of the method in [ 89 ] and the algorithm is tested also on PLI cancellation (Wavelet Neural Network—WNN). The NN based adaptive filtering approach proposed in [ 51 ] for ECG signal noise reduction removes the PLI signal by applying firstly the wavelet decomposition.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

Ţarălungă

Ungureanu

Gussi

et al. 2014

Computational and Mathematical Methods in Medicine

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Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

Ţarălungă

Ungureanu

Gussi

et al. 2014

Computational and Mathematical Methods in Medicine

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“…Deep learning has attracted more and more in-depth research studies in the field of ECG noise reduction. The representative ones are the Functional Link Neural Network (FLNN) [20], Wavelet Neural Network (WNN) [21], and Denoising Autoencoder (DAE) [22]. Among them, FLNN and WNN are the most widely used, but they can remove only one type of ECG signal.…”

Section: Introductionmentioning

confidence: 99%

An ECG Denoising Method Based on the Generative Adversarial Residual Network

Liu

Shu

et al. 2021

Computational and Mathematical Methods in Medicine

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High-quality and high-fidelity removal of noise in the Electrocardiogram (ECG) signal is of great significance to the auxiliary diagnosis of ECG diseases. In view of the single function of traditional denoising methods and the insufficient performance of signal details after denoising, a new method of ECG denoising based on the combination of the Generative Adversarial Network (GAN) and Residual Network is proposed. The method adopted in this paper is based on the GAN structure, and it restructures the generator and discriminator. In the generator network, residual blocks and Skip-Connecting are used to deepen the network structure and better capture the in-depth information in the ECG signal. In the discriminator network, the ResNet framework is used. In order to optimize the noise reduction process and solve the lack of local relevance considering the global ECG problem, the differential function and overall function of the maximum local difference are added in the loss function in this paper. The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm. Experiments show that the Root Mean Square Error (RMSE) of this method in the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) noise pressure database is 0.0102, and the Signal-to-Noise Ratio (SNR) is 40.8526 dB, which is compared with that of the most advanced experimental methods. Our method improves the SNR by 88.57% on average. Besides the three noise intensities for comparison experiments, additional noise reduction experiments are also performed under four noise intensities in our paper. The experimental results verify the scientific nature of the model, which is that our method can effectively retain the important information conveyed by the original signal.

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“…Traditional analog and digital filters were found to suppress ECG components near to 50 Hz frequency. Different types of infinite impulse response filters (IIR) and finite impulse response filters (FIR) with unacceptably long transient time were widely used to reduce PLI noises [5,6,7] . Also determination of cut-off frequency for these filters was not so easy.…”

Section: Introductionmentioning

confidence: 99%

Approximation of ECG Signals Using Chebyshev Nodes and Lagrange-interpolation

Yadav¹,

Ray²

2019

Am. J. Biomed. Sci.

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An ECG (Electrocardiography) is a graphical representation of electrical behavior of heart and is measured by placing electrodes on specific locations of limbs and chest. The characteristics of these signals change during acquisition and transmission due to addition of noises of variable frequency and amplitude. These noises need to be removed for better clinical evaluation. In this paper, ECG signals of MIT-BIH database have been approximated through a series of steps, i.e., denoising using total variation, segmentation using Bottom Up approach and finally utilizing Chebyshev nodes for Lagrange interpolation method. This paper also explores the concept and the benefit of second difference total variation over the first difference. The performance of the method is analyzed in terms of mean absolute deviation, root mean square deviation, percentage root mean square difference error, signal to noise ratio and cross correlation. The results obtained are found to be better than exiting techniques.

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Electrocardiogram (ECG) signal modeling and noise reduction using wavelet neural networks

Cited by 22 publications

References 5 publications

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

An ECG Denoising Method Based on the Generative Adversarial Residual Network

Approximation of ECG Signals Using Chebyshev Nodes and Lagrange-interpolation

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