International Journal of Psychological and Brain Sciences

| Peer-Reviewed |

Analysis on ECG Data Compression Using Wavelet Transform Technique

Received: 08 October 2017    Accepted: 18 October 2017    Published: 22 December 2017
Views:       Downloads:

Share This Article

Abstract

Although digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted. ECG compression methods can be classified into two categories; direct method and transform method. A wide range of compression techniques were based on different transformation techniques. In this work, transform based signal compression is proposed. This method is used to exploit the redundancy in the signal. Wavelet based compression is evaluated to find an optimal compression strategy for ECG data compression. The algorithm for the one-dimensional case is modified and it is applied to compress ECG data. A wavelet ECG data code based on Run-length encoding compression algorithm is proposed in this research. Wavelet based compression algorithms for one-dimensional signals are presented along with the results of compression ECG data. Firstly, ECG signals are decomposed by discrete wavelet transform (DWT). The decomposed signals are compressed using thresholding and run-length encoding. Global and local thresholding are employed in the research. Different types of wavelets such as daubechies, haar, coiflets and symlets are applied for decomposition. Finally the compressed signal is reconstructed. Different types of wavelets are applied and their performances are evaluated in terms of compression ratio (CR), percent root mean square difference (PRD). Compression using HAAR wavelet and local thresholding are found to be optimal in terms of compression ratio.

DOI 10.11648/j.ijpbs.20170206.12
Published in International Journal of Psychological and Brain Sciences (Volume 2, Issue 6, December 2017)
Page(s) 127-140
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

ECG, Compression Technique, Wavelet Transform Technique, Biomedical Engineering, Signal Processing, Biomedical Science

References
[1] Benzid R., Marir F., Boussaad A., Benyoucef M., and Arar D., Fixed percentage of wavelet coefficients to be zeroed for ECG compression, Electronics Letters, vol. 39, 830–831, 2003.
[2] Chen, X., Wang W. and Wei. G, Impact of HTTP Compression on the Web Response Time in Asymmetrical Wireless network, Proc. Of International Conference on Network Security, Wireless Communication and trusted Computing, Vol. 1, pp. 679-682, 2009.
[3] Liu, Z., Saifullah, Y., Greis M. and Sreemanthula, S., HTT Compression Techniques, IEEE conf. Proc. Of Wireless Communication and net working, Vol. 4, pp. 2495-2500, 2005.
[4] A. G. Ramakrishna and S. Saha, ECG coding by wavelet-based linear prediction, IEEE Trans. Biomed. Eng., Vol. 44, No. 12, pp. 1253–1261, 1997.
[5] Ajay Bharadwaj, Accurate ECG Signal Processing, Published in EE Times Design, February 2011. http://www.eetimes.com/design.
[6] S. M. Joseph., Spoken Digit Compression Using Wavelet Packet, IEEE Signal and Image Processing (ICSIP). pp. 255-259, Chennai, India, Dec. 15-17, 2010.
[7] Prof. Mohammed Abo-Zahhad, ECG Signal Compression Using Discrete Wavelet Transform, ISBN: 978-953-307-185-5, (2011).
[8] Storer, James A., Data Compression: Methods and Theory, Computer Science Press, Rockville, MD, 1988.
[9] Zou H. and Tewfik A. H., Parameterization of compactly supported orthonormal wavelets, IEEE Trans. on Signal Processing, vol. 41, no. 3, 1428-1431, March 1993.
[10] Zigel Y., Cohen A., and Katz A., The Weighted Diagnostic Distortion (WDD) Measure for ECG Signal Compression, IEEE Trans. Biomed. Eng., 47, 1422-1430, 2000.
[11] Abdelaziz Ouamri, ECG Compression method using Lorentzia function model, Science Direct, Digital Signal Processing 17, 8th August 2006.
[12] http://www.iki.fi/a1bert/.
[13] Anonyms, Data Compression, 21 October, (2012).
[14] http://en.wikibooks.org/wiki/DataCompression/Dictionarycompression.
[15] Anonyms, Discrete Wavelet Transform, Sep 01, 2012.
[16] http://www.thepolygoners.com/tutorials/dwavelet/DWTTut.html.
[17] Anubhuti Khare, Manish Saxena, Vijay B. Nerkar, “ECG Data Compression Using DWT”, Volume-1, Issue-1, October 2011.
[18] Dr. Juuso T. Olkkonen (Ed.), Discrete Wavelet Transforms - Theory and Applications, ISBN: 978-953-307-185-5, In Tech, Available from: (2011).
[19] http://www.intechopen.com/books/discrete-wavelet-transforms-theory-and-applications/ecg-signal-compression-using-discrete-wavelet-transform.
[20] Pranob K Charles, Rajendra Prasad K, A Contemporary Approach for ECG Signal Compression using Wavelet Transforms”, Vol. 2, No. 1, March 2011.
[21] Ranjeet Kumar, Rajesh Gautam and Anil Kumar, HTTP Compression for 1-D signal based on Multiresolution Analysis and Run length Encoding, 2011 International Conference on Information and Electronics Engineering IPCSIT vol. 6, © IACSIT Press, Singapore. (2011).
[22] Vinod kumar Professor, Application of Wavelet Techniques in ECG Signal Processing: An Overview, ISSN: 0975-5462 Vol. 3 No. 10 October 2011. Mohammed Abo-Zahhad (2011).
[23] Chun-Lin, Liu, A Tutorial of the Wavelet Transform, February 23, 2010.
[24] Abdelaziz Ouamri, Data Compression Techniques, Dec 2008.
[25] Javaid R., Besar R., Abas F. S., Performance Evaluation of Percent Root Mean Square Difference for ECG Signals Compression. Signal Processing, An International Journal (SPIJ), 1–9, April, 2008.
[26] A. Al-Shroud, M. Abo-Zahhad and S. M. Ahmed, A novel compression algorithm for electrocardiogram signals based on the linear prediction of the wavelet coefficients, Digital Signal Processing, Vol. 13, pp. 604–622, 2003.
[27] Ahmed S. M., Al-Shrouf A. and Abo-Zahhad M., ECG data compression optimal non-orthogonal wavelet transform, Med. Eng. Phys. 22, 39–46, 2000.
[28] Pasi Ojala Pasi 'Ojala, Compression Basics, 19.11.1997.
[29] Jalaleddine SMS, Hutchens CG, Strattan RD, Coberly W A, ECG data compression technique-a unified approach, IEEE transaction on Biomedical Eng, 37 (4), pp. 329-343, 1990.
Author Information
  • Department of Electronic Engineering, Yangon Technological University, Yangon, Myanmar

  • Department of Research and Innovation, Ministry of Education, Yangon, Myanmar

  • Department of Research and Innovation, Ministry of Education, Yangon, Myanmar

Cite This Article
  • APA Style

    Hla Myo Tun, Win Khaing Moe, Zaw Min Naing. (2017). Analysis on ECG Data Compression Using Wavelet Transform Technique. International Journal of Psychological and Brain Sciences, 2(6), 127-140. https://doi.org/10.11648/j.ijpbs.20170206.12

    Copy | Download

    ACS Style

    Hla Myo Tun; Win Khaing Moe; Zaw Min Naing. Analysis on ECG Data Compression Using Wavelet Transform Technique. Int. J. Psychol. Brain Sci. 2017, 2(6), 127-140. doi: 10.11648/j.ijpbs.20170206.12

    Copy | Download

    AMA Style

    Hla Myo Tun, Win Khaing Moe, Zaw Min Naing. Analysis on ECG Data Compression Using Wavelet Transform Technique. Int J Psychol Brain Sci. 2017;2(6):127-140. doi: 10.11648/j.ijpbs.20170206.12

    Copy | Download

  • @article{10.11648/j.ijpbs.20170206.12,
      author = {Hla Myo Tun and Win Khaing Moe and Zaw Min Naing},
      title = {Analysis on ECG Data Compression Using Wavelet Transform Technique},
      journal = {International Journal of Psychological and Brain Sciences},
      volume = {2},
      number = {6},
      pages = {127-140},
      doi = {10.11648/j.ijpbs.20170206.12},
      url = {https://doi.org/10.11648/j.ijpbs.20170206.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijpbs.20170206.12},
      abstract = {Although digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted. ECG compression methods can be classified into two categories; direct method and transform method. A wide range of compression techniques were based on different transformation techniques. In this work, transform based signal compression is proposed. This method is used to exploit the redundancy in the signal. Wavelet based compression is evaluated to find an optimal compression strategy for ECG data compression. The algorithm for the one-dimensional case is modified and it is applied to compress ECG data. A wavelet ECG data code based on Run-length encoding compression algorithm is proposed in this research. Wavelet based compression algorithms for one-dimensional signals are presented along with the results of compression ECG data. Firstly, ECG signals are decomposed by discrete wavelet transform (DWT). The decomposed signals are compressed using thresholding and run-length encoding. Global and local thresholding are employed in the research. Different types of wavelets such as daubechies, haar, coiflets and symlets are applied for decomposition. Finally the compressed signal is reconstructed. Different types of wavelets are applied and their performances are evaluated in terms of compression ratio (CR), percent root mean square difference (PRD). Compression using HAAR wavelet and local thresholding are found to be optimal in terms of compression ratio.},
     year = {2017}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Analysis on ECG Data Compression Using Wavelet Transform Technique
    AU  - Hla Myo Tun
    AU  - Win Khaing Moe
    AU  - Zaw Min Naing
    Y1  - 2017/12/22
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijpbs.20170206.12
    DO  - 10.11648/j.ijpbs.20170206.12
    T2  - International Journal of Psychological and Brain Sciences
    JF  - International Journal of Psychological and Brain Sciences
    JO  - International Journal of Psychological and Brain Sciences
    SP  - 127
    EP  - 140
    PB  - Science Publishing Group
    SN  - 2575-1573
    UR  - https://doi.org/10.11648/j.ijpbs.20170206.12
    AB  - Although digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted. ECG compression methods can be classified into two categories; direct method and transform method. A wide range of compression techniques were based on different transformation techniques. In this work, transform based signal compression is proposed. This method is used to exploit the redundancy in the signal. Wavelet based compression is evaluated to find an optimal compression strategy for ECG data compression. The algorithm for the one-dimensional case is modified and it is applied to compress ECG data. A wavelet ECG data code based on Run-length encoding compression algorithm is proposed in this research. Wavelet based compression algorithms for one-dimensional signals are presented along with the results of compression ECG data. Firstly, ECG signals are decomposed by discrete wavelet transform (DWT). The decomposed signals are compressed using thresholding and run-length encoding. Global and local thresholding are employed in the research. Different types of wavelets such as daubechies, haar, coiflets and symlets are applied for decomposition. Finally the compressed signal is reconstructed. Different types of wavelets are applied and their performances are evaluated in terms of compression ratio (CR), percent root mean square difference (PRD). Compression using HAAR wavelet and local thresholding are found to be optimal in terms of compression ratio.
    VL  - 2
    IS  - 6
    ER  - 

    Copy | Download

  • Sections