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Reseach Article

FPGA Realization for Baseline Wander Noise Cancellation of ECG Signals using Wavelet Transform

by Ashraf Mohammed Ali Hassan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 2
Year of Publication: 2017
Authors: Ashraf Mohammed Ali Hassan
10.5120/ijca2017914289

Ashraf Mohammed Ali Hassan . FPGA Realization for Baseline Wander Noise Cancellation of ECG Signals using Wavelet Transform. International Journal of Computer Applications. 168, 2 ( Jun 2017), 1-6. DOI=10.5120/ijca2017914289

@article{ 10.5120/ijca2017914289,
author = { Ashraf Mohammed Ali Hassan },
title = { FPGA Realization for Baseline Wander Noise Cancellation of ECG Signals using Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 2 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number2/27844-2017914289/ },
doi = { 10.5120/ijca2017914289 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:01.177587+05:30
%A Ashraf Mohammed Ali Hassan
%T FPGA Realization for Baseline Wander Noise Cancellation of ECG Signals using Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 2
%P 1-6
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Baseline Wander (BW) is a common noise in electrocardiogram (ECG). To effectively correct and to preserve more underlying components of an ECG signal, a powerful tool for removal of BW noise from various signals was introduced. This paper presented the discrete wavelet to get rid of that noise. This method is based on comparing signal with discrete multi-rate filter banks. A multi-level decomposition was performed on the noisy signal and then the splitting into low sub-bands and a high pass band sub-bands called detail level was performed. After that, the analysis of the details level and the identifying of a suitable threshold technique were done. Reconstruction of the signal was done through the calculation of the detail coefficients. Finally, the difference between the original signal and the reconstructed signal was calculated. The proposed technique was compared with the previous techniques in this domain of search. The algorithm was tested using Matlab tool. The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and adaptive filter did. The results showed also that this proposed technique achieved excellent results in terms of Mean Square Error (MSE) and convergence rate rather than the previous approaches. This paper also introduced the efficient realization of the proposed approach using FPGA. The proposed method was verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications.

References
  1. Momot, A., "Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization". Biomedical Signal process. Control,2011 4(2), PP.162-169.
  2. Xiao HU, Zhong XIAO, NI ZHANG, " Removal of baseline wander from ECG signal based on a statistical weighted moving average filter", Journal
  3. of Zeheijang University-SCIENCE C (Computers and electronics),2011, 12(5), PP. 397-403.
  4. M. M. Abo-Zahhad, A. I. Hussien, and A. M. Mohamed, "compression of ECG signal based on compressive sensing and the extraction of significant features," Int. J. communications, Network and system sciences, ,2015, PP. 97-117.
  5. H. Mamaghanian, N. Khaled, D. Atienza and P. Vandergheynst," Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes", IEEE Transactions on Biomedical Engineering, 2011, 58(9), PP. 2456-2466.
  6. H. Fathey,E. Mohamed,A. Mohamed, and W. Anis, "Enchancement of ECG signal", International journal of computer Applications , 2016, 145(7), PP. 12-6.
  7. B. Xhaja, E. Kalluci, and L. Nikolla, "Wavelet transform applied in ecg signal processing”, European scientific journal, 2015, 11(12), PP.305-312.
  8. F.chen, Chandrakasan, and Stojanovic, " Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors", IEEE journal of solid state circuit , 2012, 47(12), PP. 744-756.
  9. Leski, J.M., Henzel, N., “ECG baseline wander and powerline interference reduction using nonlinear filter bank", 2005 Signal Process., 85(4), PP.781-793.
  10. Hu, X., Xiao, Z., Liu, C.H.,"Reduction Arithmetic for Power Line Interference from ECG Based on Estimating Sinusoidal Parameters”. 3rd Int. Conf. on Biomedical Engineering and Informatics, 2010, PP. 2089-2092.
  11. Xu, L.S., Zhang, D., Wang, K.Q., Li, N.M., Wang, X.Y.,"Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter", Computer Biol. Med., 2007, 37(5), PP.716-731.
  12. Chen, H.Y., Huang, M., Jiang, Y.X., Hai, J. "Detection of ST segment in electrocardiogram by wavelet transform". Electr. Mach. Control, 2006 10(5), PP.531-533.
  13. Shi, L., Yang, C.Y., Fei, M.R., "Electrocardiogram R-wave and ST segment extraction based on wavelet transform", Chin. J. Sci. Instrum.,2008, 29(4), PP.221-227.
  14. S. E Jeroa, Palaniappan Ramua, and S.Ramakrishnanb, "Ecg steganography using curvelet transform", Biomedical signal processing and control, 2015, PP. 519-530.
  15. Guohua Lu, John-Stuart Brittain, Peter Holland, John Yianni, Alexander L. Green, John F. Stein, Tipu Z. Aziz and Shouyan Wang, "Removing ECG noise from surface EMG signals using adaptive filters", Journal of Neuroscience letters, 2009, 462, PP. 14 – 19.
  16. Sachin Singh and K.L. Yadav, "performance evaluation of different adaptive filters for ECG signal processing", International journal on computer science and engineering, 2006, 02(4), PP. 90- 93.
  17. Wilfried Philips, "Adaptive noise removal from biomedical signals using warped polynomials". IEEE transactions on biomedical engineering, 43(5), May 2013, PP. 480 – 492.
  18. Abdel-Rahaman AL- Qawasmi, and Khaled Daqrouq, “ECG signal enhancement using wavelet transform’’. WSEAS transactions on biology and biomedicine, April 2010, 7(2), PP. 62- 72.
  19. J. Mateo, C. Sanchez, C. Vaya, R. Cervigon and J. J. Rieta, “A new adaptive approach to remove baseline wander from ECG recordings using Madeline structure”. Computers in Cardiology, 2007, 34, PP. 533 – 536.
  20. Lin and Yue- Der Lin, "An adaptive algorithm for cancelin power line interference in biopotential measurement", Biomedical engineering, basis and communication, December 2004, P.P.350- 354.
  21. Sörnmo, L.,"Time-varying digital filtering of ECG baseline wander", Med. Biol. Eng. Computer, 2013, 31(5), PP. 503-508.
  22. Boucheham, B., Ferdi, Y., Batouche, M.C., "Piecewie linear correction of ECG baseline wander: a curve simplification approach", Computer. Methods Programs Biomedical, 2005,78(1), PP.1-10.
  23. Chen, H.Y., Huang, M., Jiang, Y.X., Hai, J., Detection of ST segment in electrocardiogram by wavelet transform", Electr. Mach. Control, 2006, 10(5), PP. 531-533.
Index Terms

Computer Science
Information Sciences

Keywords

Baseline Wander Electrocardiogram Filter Convergence Rate