An adaptive level dependent wavelet thresholding for ECG denoising

• Autorzy: Awal M. A. , Mostafa S. S. , Ahmad M. , Rashid M. A.

Identyfikatory

DOI

10.1016/j.bbe.2014.03.002

• Języki publikacji: EN

Abstrakty

EN

This paper describes the research carried out to eliminate the noise found in ECG signal and cardiac rhythm. For this, ECG signals were collected carefully from BIOPAC data acquisition system and MIT-BIH database. MIT-BIH noise stress test database was used for generating realistic noises. In addition, to get a better denoised ECG, Symlet wavelet was chosen because its scaling function is closely related to the shape of ECG. For denoising ECG signal, a novel modified S-median thresholding technique is proposed and evaluated in this paper. The optimal Symlet wavelet of order 6 and decomposition level of 8 are attained for modified S-median thresholding technique. The evaluation results showed that the proposed system performed better than S-median and other existing techniques in the time domain. The frequency domain analysis also showed the preservation of important phenomena of ECG. The scalogram difference of 0.004% indicates the well preservation of time–frequency information.

Słowa kluczowe

EN

ECG signal; MABWT; modified S-median; symlet wavelet; scalogram difference; S-median

PL

sygnał EKG; MABWT; falka symlet; transformacja falkowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2014
• Tom: Vol. 34, no. 4
• Strony: 238--249
• Opis fizyczny: Bibliogr. 37 poz., rys., tab., wykr.

Twórcy

autor

Awal M. A.

• Department of Biomedical Engineering, KUET, Khulna, Bangladesh

autor

Mostafa S. S.

• Department of Biomedical Engineering, KUET, Khulna, Bangladesh

autor

Ahmad M.

• Department of Biomedical Engineering, KUET, Khulna, Bangladesh

autor

Rashid M. A.

• Faculty of Design and Engineering Technology, University Sultan Zainal Abidin (UniSZA), 21300 Kuala Terengaanu, Terengganu, Malaysia

Bibliografia

• [1] Rahman MZU, Shaik RA, Reddy DVRK. Baseline wander and power line interference elimination from cardiac signals using error nonlinearity LMS algorithm. Proc. 2010 International Conference on Systems in Medicine and Biology (ICSMB). 2010. pp. 217–20.
• [2] Clifford G, Tarassenko L, Townsend N. One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats. Electron Lett 2001;37:1126–7.
• [3] Daqrouq K. ECG baseline wandering reduction using discrete wavelet transform. Asian J Inform Technol 2005;4:989–95.
• [4] Kestler H, Haschka M, Kratz W, Schwenker F, Palm G, Hombach V, et al. De-noising of high-resolution ECG signals by combining the discrete wavelet transform with the Wiener filter. Computers in Cardiology 1998. IEEE; 1998. pp. 233–6.
• [5] Moein S. An MLP neural network for ECG noise removal based on Kalman filter. Advances in computational biology. Springer; 2010. pp. 109–16.
• [6] Behrenbruch CP, Lithgow BJ. SNR improvement, filtering and spectral equalisation in cochlear implants using wavelet techniques. Proc. 1998 International Conference on Bioelectromagnetism. 1998. pp. 61–2.
• [7] Nie K, Lan N, Gao S. Implementation of CIS speech processing strategy for cochlear implants by using wavelet transform. Proc. 4th International Conference on Signal Processing Proceedings, 1998, ICSP'98. IEEE; 1998. pp. 1395–8.
• [8] Meyer C, Keiser H. Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Comput Biomed Res 1977;10:459–70.
• [9] Macfarlane P, Peden J, Lennox G, Watts M, Lawrie T. The Glasgow system. Trends in computer-processed electrocardiograms. Proc. IFIP Working Conference on Trends in Computer-Processed Electrocardiograms. 1977. pp. 143–50.
• [10] Van Alste J, Schilder T. Removal of base-line wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps. IEEE Trans Biomed Eng 1985;32:1052–60.
• [11] Tinati BMMA. ECG baseline wander elimination using wavelet packets. World Acad Sci Eng Technol 2005;3:14–6.
• [12] Adiguzel V, Durak L. Comparison of wavelet transform based techniques in the denoising of ECG signals. Proc. Signal Processing and Communications Applications, 2007, SIU 2007. IEEE; 2007. pp. 1–4.
• [13] Awal MA, Mostafa SS, Ahmad M. Quality assessment of ECG signal using Symlet wavelet transform. Proc. 2011 Advances in Electrical Engineering, International Conference. 2011. pp. 129–34.
• [14] Poornachandra S. Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Process 2008;18:49–55.
• [15] Mahmoodabadi S, Ahmadian A, Abolhasani M, Eslami M, Bidgoli J. ECG feature extraction based on multiresolution wavelet transform. Proc. 27th Annual International Conference of the Engineering in Medicine and Biology Society 2005, IEEE-EMBS 2005. IEEE; 2006. pp. 3902–5.
• [16] de Laboratóio M. Biopac Student Lab. Biopac Systems Inc.; 2008.
• [17] Moody GB, Mark RG. The MIT-BIH arrhythmia database on CD-ROM and software for use with it. Proc. 1990 Computers in Cardiology. IEEE; 1990. pp. 185–8.
• [18] Hasan M, Reaz M, Ibrahimy M, Hussain M, Uddin J. Detection and processing techniques of FECG signal for fetal monitoring. Biol Proc Online 2009;11:263–95.
• [19] Yuan X. Auditory model-based bionic wavelet transform for speech enhancement. Marquette University; 2003.
• [20] Daubechies I. Ten lectures on wavelets. SIAM; 1992.
• [21] Isa SM, Noviyanto A, Arymurthy AM. Optimal selection of wavelet thresholding algorithm for ECG signal denoising. Proc. 2011 International Conference on Advanced Computer Science and Information System (ICACSIS). 2011. pp. 365–70.
• [22] Karthikeyan P, Murugappan M, Yaacob S. ECG signal denoising using wavelet thresholding technique in human stress assessment. Proc. International Journal on Electrical Engineering and Informatics. 2012. p. 4.
• [23] Mallat S. A wavelet tour of signal processing: the sparse way. Burlington, MA: Elsevier; 2008.
• [24] Oppenheim AV, Schafer RW, Buck JR. Discrete-time signal processing. Upper Saddle River: Prentice Hall; 1999.
• [25] Daubechies I. Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 1988;41:909–96.
• [26] Arivazhagan S, Ganesan L. Texture classification using wavelet transforms. Pattern Recogn Lett 2003;24:1513–21.
• [27] Donoho DL. De-noising by soft-thresholding. IEEE Trans Inform Theory 1995;41:613–27.
• [28] Donoho DL, Johnstone JM. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994;81(3):425–55.
• [29] Chang SG, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process 2000;9:1532–46.
• [30] Fu Q, Wan EA. Perceptual wavelet adaptive denoising of speech. INTERSPEECH; 2003.
• [31] Misiti M, Misiti Y, Oppenheim G, Poggi J-M. Wavelet toolbox user's guide. The Math Works Inc.; 1996.
• [32] Yao J, Zhang Y-T. Bionic wavelet transform: a new time– frequency method based on an auditory model. IEEE Trans Biomed Eng 2001;48:856–63.
• [33] Sayadi O, Shamsollahi MB. Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction. EURASIP Journal on Advances in Signal Processing, 2007. New York: Hindawi Publishing Corporation; 2007.
• [34] Jun Y, Zhang YT. A study on inhomogeneous characteristics of cochlea for spontaneous otoacoustic emissions. [Engineering in Medicine and Biology, 1999 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society] BMES/EMBS Conference; 1999.
• [35] Manikandan MS, Dandapat S. Wavelet energy based diagnostic distortion measure for ECG. Biomed Signal Process Control 2007;2:80–96.
• [36] Welch P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 1967;15:70–3.
• [37] Awal MA, Ahmad M, Daut I, Mid E, Rashid M. Wavelet based distortion measurement and enhancement of ECG signal. Proc. 2012 International Conference on Biomedical Engineering (ICoBE). IEEE; 2012. pp. 373–8.