![]() ![]() ECG denoising using Wiener filter and Kalman filter. Journal of the Institution of Engineers (India): Series B, 2020, 101(5): 451–461. Arrhythmia detection in ECG signal using fractional wavelet transform with principal component analysis. Stationary wavelet transform based ECG signal denoising method. Noise reduction in ECG signal using an effective hybrid scheme. Engineering Applications of Artificial Intelligence, 2019, 79: 34–44.īING P P, LIU W, WANG Z, et al. Multi-lead model-based ECG signal denoising by guided filter. Electrocardiogram signal denoising by a new noise variation estimate. Biomedical Signal Processing and Control, 2021, 63: 102221. ![]() Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising. International Journal of Biomedical Engineering and Technology, 2019, 31(4): 325.ĬHANDRA M, GOEL P, ANAND A, et al. Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. System for ECG signal denoising // 2020 International Conference on Communication and Signal Processing. Wireless Communications and Mobile Computing, 2020, 2020: 1–11. An efficient ECG denoising method based on empirical mode decomposition, sample entropy, and improved threshold function. DeepCEDNet: An efficient deep convolutional encoder-decoder networks for ECG signal enhancement. Review of noise removal techniques in ECG signals. Noise reduction in ECG signals using fully convolutional denoising autoencoders. BioMed Research International, 2019, 2019: 2608547.ĬHIANG H T, HSIEH Y Y, FU S W, et al. ECG signal denoising and features extraction using unbiased FIR smoothing. LASTRE-DOMÍNGUEZ C, SHMALIY Y S, IBARRA-MANZANO O, et al. Biomedical Signal Processing and Control, 2020, 57: 101824. ECG signal denoising based on deep factor analysis. The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising. ![]() A comparison of the HBO approach with recursive least square-based adaptive filter, multichannel least means square, and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter. The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian, electromyogram and electrode motion artifact noises. The honey badge optimization (HBO) algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters. The ECG signals are denoised by the proposed adaptive hybrid filter. ![]() At first, the white Gaussian noise is added to the input ECG signal and then applied to the EWT. In this article, swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms (EWTs). Various health practitioners use the ECG signal to ascertain critical information about the human heart. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Cardiovascular diseases are the world’s leading cause of death therefore cardiac health of the human heart has been a fascinating topic for decades. ![]()
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