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VLSI Architecture for Optimization Transform Technique based on Compression of ECG Signals

by Ashraf Mohamed Ali, Waleed Elnahel, Hatem M. Zakaria
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 48
Year of Publication: 2019
Authors: Ashraf Mohamed Ali, Waleed Elnahel, Hatem M. Zakaria
10.5120/ijca2019918664

Ashraf Mohamed Ali, Waleed Elnahel, Hatem M. Zakaria . VLSI Architecture for Optimization Transform Technique based on Compression of ECG Signals. International Journal of Computer Applications. 181, 48 ( Apr 2019), 54-62. DOI=10.5120/ijca2019918664

@article{ 10.5120/ijca2019918664,
author = { Ashraf Mohamed Ali, Waleed Elnahel, Hatem M. Zakaria },
title = { VLSI Architecture for Optimization Transform Technique based on Compression of ECG Signals },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2019 },
volume = { 181 },
number = { 48 },
month = { Apr },
year = { 2019 },
issn = { 0975-8887 },
pages = { 54-62 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number48/30484-2019918664/ },
doi = { 10.5120/ijca2019918664 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:09:31.274477+05:30
%A Ashraf Mohamed Ali
%A Waleed Elnahel
%A Hatem M. Zakaria
%T VLSI Architecture for Optimization Transform Technique based on Compression of ECG Signals
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 48
%P 54-62
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The measurement of electrical activity of the heart via electrodes is named as Electrocardiography (ECG). An efficient compression technique using the compressive sensing method is required. Compressive Sensing (CS) holds the promise to be a key for acquisition and reconstruction of sparse signals. The reconstruction of such signals makes sampling rates below Nyquist rate. In this work, a novel framework was proposed that is based on the idea of CS theory for the compression of mother and fetal heart beats. The proposed scheme is based on the sparse representation of the components derived from the curvelet transform of the original Electrocardiogram (ECG) signal. The ECG signals may be approximated by a few coefficients that can be taken from a wavelet basis. This fact allows a compressed sensing approach for ECG signal compression to be introduced and to be a domain of search. ECG signals illustrate redundancy between adjacent heart beats. This redundancy implies a high fraction of common support between consecutive heart beats. The main contribution of this paper lies in the using of curvelet transform in order to generate sparsity in ECG signal. This transformation is considered an excellent approach as illustrated in this paper. Simulation results represent a better approach than Discrete Wavelet Transform (DWT) that is based on compression of ECG. MIT-BIH database is used for experimentation. The MIT-BIH database contains different kinds of ECG signals that include both abnormal ECG and normal ECG, which have different sampling rates. MATLAB tool is used for simulation purpose. The novelty of the method is that the Compression Ratio (CR) achieved by detail coefficients is better. The performance measure of the reconstructed signal is carried out by Percentage Root Mean Difference (PRD). This paper also introduces the efficient realization of the different transformation techniques using FPGA. Thus the contribution of this paper lies into two main parts. The first part is specialized in determining the proper transformation that is used in the compression of ECG signals. The second part of the contribution is summarized in using suitable hardware to implement this design. Architecture can be based on the ideas of parallelism and pipelining to get the minimum throughput and speed. Architecture is cascade and simple for calculating curvelet coefficients. The reduction of the memory size can be done by splitting ROM table. The description and functionalities of the design are modeled by Verilog HDL. The simulation and synthesis methodology are used on Virtex-II Pro FPGA that uses less number of resources of the FPGA.

References
  1. Mohammed M. Abo-Zahhad, Aziza. I. Hussein and Abdelfatah M. Mohamed, "Compressive Sensing Algorithms for Signal Processing Applications: A Survey", Int. J. Communications, Network and System Sciences 2015, Vol. 8, PP. 197-216.
  2. Yong Wang, Zhoushi Yang and Yiran Shen, "CS2-Collector: A New Approach for Data Collection in Wireless Sensor Networks Based on Two-Dimensional Compressive Sensing", Sensors 2016, Vol. 16, PP. 1-16.
  3. Matthew Hawes, Wei Liu and Lyudmila Mihaylova, "Compressive Sensing Based Design of Sparse Tripole Arrays", Sensors 2015, Vol. 15, PP. 31056-31068.
  4. Vertterli M., Marziliano P. and Blu T. S., "Sampling signals with finite rate of innovation", IEEE Transactions on Signal Processing 2002, Vol. 50, PP. 352-358.
  5. J. Cardenas-Barrera, J. Lorenzo-Ginori and E. Rodriguez-Valdivia, "A wavelet-packets based algorithm for EEG signal compression", Med. Informatic. and Internet in Med 2004, Vol. 29, PP. 15-27.
  6. Mohammed M. Abo-Zahhad, Aziza I. Hussein and Abdelfatah M. Mohamed, "Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features", Int. J. Communications, Network and System Sciences 2015, Vol. 8, PP. 97-117.
  7. Hossein Mamaghanian, Nadia Khaled, David Atienza and Pierre Vandergheynst, "Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes", IEEE tansactions on biomedical engineering 2011, Vol. 58, PP. 2456-2466.
  8. Hend Fathey, Eman Mohamed, Ashraf Mohamed and Wagdy Anis, "Enhancement of ECG Signal", International Journal of Computer Applications 2016, Vol.145, PP.12-16.
  9. Brikena Xhaja, Eglantina Kalluci and Ligor Nikolla, "Wavelet Transform Applied in ECG Signal Processing", European Scientific Journal 2015, Vol. 11, PP. 305-312.
  10. Chen F., Chandrakasan and Stojanovic, "Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors", IEEE Journal of Solid-State Circuits 2012, Vol. 47, PP. 744-756.
  11. Namrata and Vaswani, "LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual", IEEE Transactions on Signal Processing 2010, Vol. 58, PP. 944-958.
  12. S. Edward Jeroa, Palaniappan Ramua and S.Ramakrishnanb, "ECG Steganography Using Curvelet Transform", Biomedical Signal Processing and Control 2015, 35, 519-530.
  13. E. J. Cand‘es, Ph.D. thesis, Department of Statistics, Stanford University, "In Ridgelets: Theory and Applications", Editor, F., Meditor, A., Eds.; Publishing House: City, Country, 1998; PP. 32-58.
  14. E. J. Cand‘es and D. L. Donoho, Ridgelets: "A key to higherdimensional intermittency? Phil. Trans. R. Soc. Lond. A.", 1999, Vol. 10, PP. 2495-2509.
  15. S. R. Deans, "The Radon Transform and Some of Its Applications", John Wiley and Sons 1983, Vol. 10, PP. 154-169.
  16. Vijay, Gowri, Velmurugan and Dr.AM.Basha, "Detection and Extraction of P Wave and T Wave in ECG to Improve Sensitivity for E-Health Monitoring", International Journal of Communication and Computer Technologies 2016, Vol. 4, PP. 4020-4024.
  17. Mohammed Abo-Zahhad, Sabah M.Ahmed and Ahmed Zakaria, "An efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation and 2D-DWT Coefficients Thresholding", Modeling and Simulation in Engineering 2012, Vol. 20, PP. 1-10.
  18. Y. Eldar and M. Mishali, "Robust recovery of signals from a structured union of subspaces", IEEE Transactions Info. Theory, 2009, Vol. 55, PP. 5302-5316.
  19. Lu., Z., Kim and Pearlman, "Wavelet Compression of ECG Signals by the Set Partitioning in Hierarchical Trees Algorithm", IEEE Transactions on Biomedical Engineering 2000, Vol. 47, PP. 849-856.
  20. Luisa F. Polania, Rafael E. Carrillo, Manuel Blanco-Velasco and Kenneth E. Barner, "Compressed Sensing Based Method for ECG Compression", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2011, PP. 761-764.
  21. Bharti Salsekar, "Filtering of ECG Signal Using Butterworth Filter and its Feature Extraction", International Journal of Engineering Science and Technology 2018, Vol. 4, PP. 1292-1298.
  22. Surekha K.S. and B.P. Patil, "ECG Signal Compression Using the High Frequency Components of Wavelet Transform", International Journal of Advanced Computer Science and Applications 2016, Vol. 7, PP. 311-315.
  23. Mile Petkovski, Sofija Bogdanova, Momcilo Bogdanov, "A Simple Adaptive Sampling Algorithm", 14th Telecommunications forum TELFOR 2006, PP. 329-332.
  24. Skander Bensegueni and Abdelhak Bennia, "ECG Signal Compression Using a Sinusoidal Transformation of Principal Components", International Journal of Software engineering and its applications 2017, Vol. 10, PP. 59-68.
  25. Surekha K.S. and B. P. Patil, "Transform Based Techniques for ECG Signal Compression", International Journal of Signal Processing, Image Processing and Pattern Recognition 2016, Vol. 11, PP. 6139-6143.
  26. P. S. Hiremath, S. Shivashankar and Jagadeesh Pujari, "Wavelet Based Features for Color Texture Classification with Application to CBIR", International Journal of Computer Science and Network Security (IJCSNS) 2006, Vol. 6, PP. 101-106.
  27. S. Arulselvi and P. Mangaiyarkarasi, "A New Digital Image Watermarking Based on Finite Ridgelet Transform and Extraction Using ICA", DETECT IEEE 2011, Vol. 34, PP. 837-841.
  28. M. Y. Abbass, S. El-Rabaie and F. E. Abd El-Samie, "Efficient Blind Image Separation Using Finite Ridgelet Transform", ICCTA, 2013, PP. 29-31.
  29. Karishma Qureshi and V. P. Patel, "Efficent Data Compression of ECG Signal Using Discrete Wavelet Transform", International Journal of Research in Engineering and Technology, 2013, Vol. 2, PP. 696-699.
  30. Desai Siddhi and Nakrani Naitik, "Improved Performance of Compressive Sensing for Speech Signal with Orthognal Symmetric Toeplitz Matrix", International Journal of Signal Processing, Image Processing and Pattern Recognition, 2017, Vol. 7, PP. 371-380.
  31. Akanksha Mishra, Falgun Thakkar, Chintan Modi and Rahul Kherl, "Comparative Analysis of Wavelet Basis Functions for ECG Signal Compression through Compressive Sensing", International Journal of Computer Science and Telecommunications, 2012, Vol. 3, PP. 23-51.
  32. L. Bai, P. Maechler, M. Muehlberghuber, and H. Kaeslin, "High speed compressed sensing reconstruction on FPGA using OMP and AMP", in Proceedings of the 19th IEEE International Conference on Electronics, Circuits, and Systems (ICECS ’12), IEEE, Seville, Spain, December 2012, pp. 53–56.
  33. J. L. V. M. Stanislaus and T. Mohsenin, "Low-complexity FPGA implementation of compressive sensing reconstruction", in Proceedings of the International Conference on Computing, Networking and Communications (ICNC ’13), January 2013 pp. 671–675.
  34. F.B.Ren, R.Dorrace, W.Y.Xu, and D.Markovi´c, "A single-precision compressive sensing signal reconstruction engine on FPGAs", in Proceedings of the 23rd International Conference on Field Programmable Logic and Applications (FPL ’13), IEEE, Porto, Portugal, September 2013, pp.1–4.
Index Terms

Computer Science
Information Sciences

Keywords

Compressive sensing Sparse Sampling rates Heart beats Compression ratio FPGA

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