ECG Data Compression using Wavelet Transform

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-9 Number-15
Year of Publication : 2014
Authors : Suresh Patel , Dr. Ashutosh Datar
  10.14445/22315381/IJETT-V9P346

Citation 

Suresh Patel , Dr. Ashutosh Datar. "ECG Data Compression using Wavelet Transform", International Journal of Engineering Trends and Technology (IJETT), V9(15),770-776 March 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

ECG data compression has been one of the active research areas in biomedical engineering. In this paper a compression method for electrocardiogram (ECG) signals using wavelet transform is proposed. Wavelet transform compact the energy of signal in fewer samples and has a good localization property in time and frequency domain. The MIT-BIH ECG signals are decomposed using discrete wavelet transform (DWT).The DWT provide powerful capability to remove frequency components at specific time in the data. The thresholding of the resulted DWT coefficients are done in a manner such that a predefined goal percent root mean square difference (GPRD) is achieved. The compression is achieved by the quantization technique, run-length encoding, Huffman and binary encoding methods. The proposed method, for fixed GPRD shows better performance with high compression ratios and good quality reconstructed signals.

References

[1] S. M. S. Jalaeddine, C. G. Hutchens, R. D. Strattan, and W. A. Coberly, “ECG compression techniques —A unified approach,” IEEE Trans. Biomed. Eng., vol. 37, pp. 329–343, Apr. 1990.
[2] S. G. Miaou and H. L. Yen, “Quality-driven gold washing adaptive vector quantization and its application to ECG data compression,”IEEE Trans. Biomed. Eng., vol. 47, pp. 209–218, Feb. 2000.
[3] J. R. Cox, F. M. Nolle, H. A. Fozzard, and G. C. Oliver, “AZTEC: A preprocessing scheme for real time ECG rhythm analysis,” IEEE Trans. Biomed. Eng., vol. BME-15, pp. 128–129, Apr. 1968.
[4] D. Stewart, G. E. Dower, and O. Suranyi, “An ECG compression code,”J. Electrocardiol., vol. 6, no. 2, pp. 175–176, 1973.
[5] U.E. Ruttimann and H. V. Pipberger, “Compression of ECG by prediction or interpolation and entropy encoding,” IEEE Trans. Biomed. Eng.,vol. BME-26, pp. 613–623, Nov. 1979.
[6] B. R. S. Reddy and I. S. N. Murthy, “ECG data compression using Fourier descriptors,” IEEE Trans. Biomed. Eng., vol. BME-33, pp.428–434, Apr. 1986.
[7] W.S. Kuklinski, “Fast Walsh transform data-compression algorithm: ECG applications,” Med. Biol. Eng. Comput., vol. 21, pp. 465–472, July 1983.
[8] S. Olmos, M. Millan, J. Garcia, and P. Laguna, “ECG data compression with the Karhunen–Loeve transform,” in Comput. Cardiol. Conf., 1996, pp. 253–256.
[9] B. Bradie, “Wavelet packet-based compression of single lead ECG,”IEEE Trans. Biomed. Eng., vol. 43, pp. 493–501, May 1996.
[10] A. G. Ramakrishnan and S. Saha, “ECG coding by wavelet-based linear prediction,” IEEE Trans. Biomed. Eng., vol. 44, pp. 1253–1261, Dec.1997.
[11] M. L. Hilton, “Wavelet and wavelet packet compression of electrocardiograms,” IEEE Trans. Biomed. Eng., vol. 44, pp. 394–402, May 1997.
[12] J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Processing, vol. 41, pp. 3445–3462, Dec.1993.
[13] A. Said and W. A. Pearlman, “A new, fast and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video Technol., vol. 6, pp. 243–250, June 1996.
[14] Z. Lu, D. Y. Kim, and W. A. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm,” IEEE Trans. Biomed. Eng., vol. 47, pp. 849–856, July 2000.
[15] S-G. Miaou, C-L. Lin, “A Quality-on-demand algorithm for wavelet-based compression of electrocardiogram Signals,” IEEE Trans. on Biomedical Engineering, vol. 49, pp. 233-239, 2002.
[16] B. A. Rajoub, “An efficient coding algorithm for the compression of ECG signals using the wavelet transform,” IEEE Trans. on Biomedical Engineering, vol. 49, pp. 355-362, 2002.
[17] M. Abo-Zahhad and B. A. Rajoub, “ An effective coding technique for the compression of one-dimensional signals using wavelet transform,” Medical Engineering & Physics, vol. 24 pp. 185-199, 2002.
[18] S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Patten Anal. Machine Intell., vol.11, pp. 674–693, 1989.
[19] Y. Zigel, A. Cohen, and A. Katz, “The weighted diagnostic distortion (WDD) measure for ECG signal compression,” IEEE Trans. Biomed. Eng., vol. 47, pp. 1422–1430, 2000.
[20] K. Sayood, Introduction to Data Compression. San Mateo, CA: Morgan Kaufmann, 1996.
[21] R. Benzid, F. Marrir and N. E. Bouguechal, “Quality-controlled compression method using wavelet transform for electrocardiogram signals,” International Journal of Biological and Life Sciences, vol. 1. pp. 28-33, 2005.
[22] P. P. Vaidyanathan, Multirate Systems and Filter Banks. Englewood Cliffs, NJ: Prentice-Hall, 1993.
[23] I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math., vol. 41, no. 7, pp. 909–996, November 1988.
[24] I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA: Soc. Ind. Appl. Math. (SIAM), 1992, SIAM CBMS-NSF Regional Conf.: Applied Mathematics.
[25] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C—The Art of Scientific Computing. Cambridge, U.K.: Cambridge Univ. Press, 1988.
[26] J. Max, "Quantizing for Minimum Distortion," IRE Transactions on Information Theory, Vol. 6, pp. 7-12. 1960.
[27] S. P. Lloyd, "Least Squares Quantization in PCM," IEEE Transactions on Information Theory, Vol. 28, pp. 129-137. 1982.
[28] A. Djohan, T. Q. Nguyen, and W.J. Tompkins, “ECG compression using discrete symmetric wavelet transform,” presented at the 17th IEEE Int. Conf. Medicine and Biology, Montreal, QC, Canada, 1995.

Keywords
Compression, discrete wavelet transform Electrocardiogram (ECG), PRD, quantization, thresholding.