ElectroCardioGram signal denoising using Discrete Wavelet Transform

Hanine, M.; Abdelmounim, Elhassane; Haddadi, Rachid; Belaguid, Abdelaziz

doi:10.1109/icmcs.2014.6911270

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…The wavelet transforms denoising method has been proposed in the early years and has got good results. In this study, we adopt a new wavelet transform denoising method based on multi-layer decomposition analysis ( El hanine et al, 2020 ; Li et al, 2021 ). The emphasis of this method is to select a new threshold rule for sEMG reconstruction and denoising.…”

Section: Methodsmentioning

confidence: 99%

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Lu,

Gong,

Xue

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method.Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination.Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

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Section: Methodsmentioning

confidence: 99%

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Lu,

Gong,

Xue

et al. 2024

Front. Bioeng. Biotechnol.

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“…This can be addressed by exploiting the advantage of Zero phase filter bank [26]. Another popular approach is signal decomposition using discrete wavelet transforms [27][28][29] for ECG denoising, but noise is present at various levels of detailed coefficients. Thus, removing these coefficients eliminates the noise and sometimes it leads to loss required information also.…”

Section: Fourier Decomposition Methods For Noise Suppressionmentioning

confidence: 99%

An Improved R-Peaks Marking Method Using Fourier Decomposition and Teager Energy Operator

Harikrishna¹,

Shaik²

2020

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The exact discovery of R-peak becomes very much crucial while extracting prominent features from Electrocardiogram (ECG) signal. However, identification of R-peaks precisely becomes more challenging due to contamination of noise and fragmented QRS complexes. This paper presents an improved method of marking R-peaks. Initially, an efficient Fourier Decomposition Methodology (FDM) is used for removing noise. The accuracy of finding R-peaks can be improved by enhancing the QRS complexes using Teager Energy Operator. Hilbert Transform and Zero Cross Detector (ZCD) are used for marking the R-peaks. The MIT-BIH arrhythmia database is used for validating the proposed scheme and attained 99.97% accuracy, 99.98% of sensitivity and 99.98% of positive predictivity. The findings proved that proposed method is superior as compared to the proven techniques in the literature.

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“…Taking into consideration that BW correction and denoising are considered individually in most existing technologies, the researchers design the methods with combination of the both for efficiency. In [25] a method based on wavelet is proposed, but wavelet transform cannot effectively remove the smooth varying BW interference. In [26], training dictionary is used for sparse representation of given ECG signals to learn more signal details, however, the algorithm is time-consuming, and the peak underestimation is still obvious.…”

Section: Introductionmentioning

confidence: 99%

ECG Baseline Wander Correction and Denoising Based on Sparsity

et al. 2019

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To reduce the influence of both the baseline wander (BW) and noise in the electrocardiogram (ECG) is much important for further analysis and diagnosis of heart disease. This paper presents a convex optimization method, which combines linear time-invariant filtering with sparsity for the BW correction and denoising of ECG signals. The BW signals are modeled as low-pass signals, while the ECG signals are modeled as a sequence of sparse signals and have sparse derivatives. To illustrate the positive of the ECG peaks, an asymmetric function and a symmetric function are used to punish the original ECG signals and their difference signals, respectively. The banded matrix is used to represent the optimization problem, in order to make the iterative optimization method more computationally efficient, take up the less memory, and apply to the longer data sequence. Moreover, an iterative majorization-minization algorithm is employed to guarantee the convergence of the proposed method regardless of its initialization. The proposed method is evaluated based on the ECG signals from the database of MIT-BIH Arrhythmia. The simulation results show the advantages of the proposed method compared with wavelet and median filter. INDEX TERMS Baseline wander correction, ECG denoising, convex optimization, sparsity.

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ElectroCardioGram signal denoising using Discrete Wavelet Transform

Cited by 14 publications

References 10 publications

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

An Improved R-Peaks Marking Method Using Fourier Decomposition and Teager Energy Operator

ECG Baseline Wander Correction and Denoising Based on Sparsity

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