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Biocybernetics and Biomedical Engineering

Tytuł artykułu

Denoising of ECG signal by non-local estimation of approximation coefficients in DWT

Autorzy Singh, P.  Pradhan, G.  Shahnawazuddin, S. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN This paper presents an ECG denoising technique using merits of discrete wavelet transform (DWT) and non-local means (NLM) estimation. The NLM-based approach is quite effective in removing low frequency noises but it suffers from the issue of under-averaging in the high-frequency QRS-complex region. In addition to that, the computational cost of NLM estimation is also high. The DWT, on the other hand, is effective in removing high-frequency noise but needs larger decomposition levels in order to denoise the low-frequency components. Thresholding lower-frequency components in the DWT domain often results in a loss of critical information. To overcome these drawbacks, in the proposed method, two-level DWT decomposition is first performed in order to decompose the noisy ECG signal into low- and high-frequency approximation and detail coefficients, respectively, at each level. The high frequency noise is removed by thresholding the detail coefficients at both the levels. The noise in the lower-frequency region is then removed by performing NLM estimation of Level 2 approximation coefficient. The Level 2 approximation coefficients actually represent the low-frequency envelope of the ECG. Thus, the proposed technique effectively combines the power of both NLM and DWT. At the same time, the computational cost of whole process is not more than the earlier existing techniques since NLM estimation is performed only on Level 2 approximation coefficients instead of the complete ECG signal. The proposed method is found to be superior to the existing state-of-the-art techniques when tested on the MIT-BIH arrhythmia database.
Słowa kluczowe
PL elektrokardiogram   dyskretna transformata falkowa   współczynnik aproksymacji   współczynnik szczegółowości  
EN electrocardiogram   nonlocal means   discrete wavelet transform   approximation coefficient   detail coefficient  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 599--610
Opis fizyczny Bibliogr. 35 poz., rys., tab., wykr.
autor Singh, P.
  • Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna 800005, India,
autor Pradhan, G.
  • Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna 800005, India,
autor Shahnawazuddin, S.
  • Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna 800005, India,
[1] Clifford G, Azuaje F, McSharry P. Advanced methods and tools for ECG data analysis. London: Artech House; 2006.
[2] Rangayyan RM. Biomedical signal analysis: a case-study approach. Wiley; 2011.
[3] Wang X, Gui Q, Liu B, Jin Z, Chen Y. Enabling smart personalized healthcare: a hybrid mobile-cloud approach for ECG telemonitoring. IEEE J Biomed Health Inform 2014;18:739–45.
[4] Wen C, Yeh M-F, Chang K-C, Lee R-G. Real-time ECG telemonitoring system design with mobile phone platform. Measurement 2008;41:463–70.
[5] Bansal D, Khan M, Salhan AK. A computer based wireless system for online acquisition, monitoring and digital processing of ECG waveforms. Comput Biol Med 2009;39:361–7.
[6] Liu B, Zhang Z, Xu G, Fan H, Fu Q. Energy efficient telemonitoring of physiological signals via compressed sensing: a fast algorithm and power consumption evaluation. Biomed Signal Process Control 2014;11:80–8.
[7] Zhang Z, Jung TP, Makeig S, Rao BD. Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning. IEEE Trans Biomed Eng 2013;60:300–9.
[8] Tracey B, Miller E. Nonlocal means denoising of ECG signals. IEEE Trans Biomed Eng 2012;59:2383–6.
[9] Kabir M, Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Process Control 2012;7:481–9.
[10] Smital L, Vítek M, Kozumplik J, Provaznik I. Adaptive wavelet wiener filtering of ECG signals. IEEE Trans Biomed Eng 2013;60:437–45.
[11] Addison P. Wavelet transforms and the ECG: a review. Physiol Meas 2005;26:R155.
[12] Singh B, Tiwari A. Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process 2006;16:275–87.
[13] Seljuq U, Himayun F, Rasheed H. Selection of an optimal mother wavelet basis function for ECG signal denoising. IEEE. 2014. pp. 26–30.
[14] Sharma LN, Dandapat S, Mahanta A. ECG signal denoising using higher order statistics in wavelet subbands. Biomed Signal Process Control 2010;5:214–22.
[15] Agante P, De Sá JM. ECG noise filtering using wavelets with soft-thresholding methods. Proceedings of Computers in Cardiology; 1999, September. p. 535–8.
[16] Alfaouri M, Daqrouq K. ECG signal denoising by wavelet transform thresholding. Am J Appl Sci 2008;5:276–81.
[17] Sayadi O, Shamsollahi MB. Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction. EURASIP J Adv Signal Process 2007;1:1–11.
[18] Velasco MB, Weng B, Barner KE. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput Biol Med 2008;38:1–13.
[19] Pal S, Mitra M. Empirical mode decomposition based ECG enhancement and QRS detection. Comput Biol Med 2012;42:83–92.
[20] Boudraa A, Cexus J. EMD-based signal filtering. IEEE Trans Instrum Meas 2007;56:2196–202.
[21] Chacko A, Ari S. Denoising of ECG signals using empirical mode decomposition based technique. 2012 International Conference on Advances in Engineering, Science and Management (ICAESM). 2012. pp. 6–9.
[22] Kopsinis Y, McLaughlin S. Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans Signal Process 2009;57:1351–62.
[23] Lahmiri S. Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthcare Technol Lett 2014;1:104.
[24] Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR. 2005. pp. 60–5.
[25] Samadi S, Shamsollahi M. ECG noise reduction using empirical mode decomposition based on combination of instantaneous half period and soft-thresholding. IEEE Middle East Conference on Biomedical Engineering (MECBME). 2014. pp. 244–8.
[26] Sharma R, Prasanna S. A better decomposition of speech obtained using modified empirical mode decomposition. Digit Signal Process 2016;58:26–39.
[27] Donoho D. De-noising by soft-thresholding. IEEE Trans Inf Theory 1995;41:613–27.
[28] Donoho D, Johnstone I. Minimax estimation via wavelet shrinkage. Ann Stat 1998;26:879–921.
[29] Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 1989;11:674–93.
[30] Van De Ville, D., Kocher M. SURE-based non-local means. IEEE Signal Process Lett 2009;16:973–6.
[31] Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one. Multiscale Model Simul 2005;4:490–530.
[32] Deledalle C, Duval V, Salmon J. Non-local methods with shape-adaptive patches (NLM-SAP). J Math Imaging Vis 2012;43:103–20.
[33] Goldberger AL, Amaral L, Glass L, Hausdorff J, Ivanov P, Mark R, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101:e215–20.
[34] Stein C. Estimation of the mean of a multivariate normal distribution. Ann Stat 1981;9:1135–51.
[35] Van De Ville D, Kocher M. Nonlocal means with dimensionality reduction and SURE-based parameter selection. IEEE Trans Image Process 2011;20:2683–90.
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-68253531-521f-4e72-b22d-8b0f21f101ba
DOI 10.1016/j.bbe.2017.06.001