In this paper we are to find the optimum multiwavelet for compression of electrocardiogram (ECG) signals and then, selecting it for using with SPIHT codec. At present, it is not well known which multiwavelet is the best choice for optimum compression of ECG. In this work, we examine different multiwavelets on 24 sets of ECG data with entirely different characteristics, selected from MIT-BIH database. For assessing the functionality of the different multiwavelets in compressing ECG signals, in addition to known factors such as Compression Ratio (CR), Percent Root Difference (PRD), Distortion (D), Root Mean Square Error (RMSE) in compression literature, we also employed the Cross Correlation (CC) criterion for studying the morphological relations between the reconstructed and the original ECG signal and Signal to reconstruction Noise Ratio (SNR). The simulation results show that the Cardinal Balanced Multiwavelet (cardbal2) by the means of identity (Id) prefiltering method to be the best effective transformation. After finding the most efficient multiwavelet, we apply SPIHT coding algorithm on the transformed signal by this multiwavelet.
[1] S. M. S. Jalaleddine, C. G. Hutchens, R. D. Strattan, and W. A.
Coberly, "ECG data compression techniques- a unified approach,"
IEEE Trans. Biomed. Eng., vol. 37, no. 4, pp. 329-343, Apr. 1990.
[2] M. Cotronei, L. B. Montefosco , and L. Puccio, "Multiwavelet
analysis and Signal Processing," IEEE Trans. Circuit and System,
vol.45,no.8, pp. 970-987, Aug.1998
[3] V. Strela, P.N. Heller, G. Strang, P. Topiwala, C. Heil, "The
application of multiwavelet filter banks to image processing,"
IEEE Trans. Image processing, vol. 8(4), pp.548-563, April 1999
(Also Technical Report, MIT, Jan. 1996).
[4] V. Strela, Multiwavelets: Theory and Application, PhD. Thesis,
MIT, 1996.
[5] H. Soltanian-Zadeh and K. Jafari-khouzani, "Multiwavelet gradind
of prostate pathological images," Proceedings of SPIE Medical
Imaging conference, San Diego, CA, Feb. 2002.
[6] P. N. Heller, V. Strela, G. strang, P. Topiwala, C. Heil, and L. S.
Hills, "Multiwavelet filter banks for data compression," IEEE
proc. of the Int. symp. on Circuits and System, pp. 1796-1799,
1995.
[7] M. Cotronei, D. Lazzaro, L. B. Montefusco, and L. Puccio, "Image
Compression Through Embedded Multiwavelet Transform
Coding," IEEE Trans. Image Proc., vol. 9, No. 2, pp.184-189,
Feb. 2000.
[8] T. R. Dowine, and B. W. silverman, "The discrete multiple
wavelet transform and thresholding methods," Technical Report,
University of Bristol, November 1996 (also in IEEE Trans. On
Signal Processing, vol.46, pp. 2558-2561, 1998).
[9] V. Strela and A. T. Walden, "Signal and image denoising via
wavelet thresholding: Orthogonal and biorthogonal, scaler and
multiple wavelet transform," Imperial College, Statistics Section,
Technical Report, TR-98-01, 1998.
[10] T. N. T. Goodman and S. L. Lee, "Wavelet of Multiplicity r",
Trans. Amer. Math Soc., vol. 342, pp. 307-329, 1994.
[11] X. G. Xia, "A New prefilter Design for Discrete Multiwavelet
transforms," IEEE Trans. Signal Processing, vol. 46, No. 6,
pp.1558-1570, 1998.
[12] G. Plonka and V. Strela, "From wavelet to multiwavelets," Math
Methods for Curves and Surf. II., M. Dahlem, T. Lyche, L.
Shumaker (eds), Vanderblt University Press, pp.375-399, 1998.
[13] W. Selesnick, "Cardinal multiwavelets and the sampling theorem,"
Proc. Of IEEE Int. Conf. Acoustics , Speech, and Signal
Processing, vol. 3, pp. 12109-1212, 1999.
[14] X. G. Xia, J. S. Geronimo, D. P. Hardin, and B. W. Suter, "Design
of prefilters for discrete multiwavelet transforms," IEEE Trans.
Signal Processing, vol. 44, No. 1, pp.25-35, 1996.
[15] Zhito Lu, Dong Yong kim, Pearlman, W.A. "Wavelet compression
of ECG signals by the set partitioning in hierarchical trees
algorithm," IEEE Trans. Biomed. Eng., vol. 47, No. 7, pp. 849-
856, July 2000.
[16] Said, W. A. Pearlman, "A New, Fast and Efficient Image Codec
Based on Set Partitioning in Hierarchical Tress", IEEE Trans.
Circ. Sys. Vid. Tech., vol. 6, pp. 243-250, June 1996
[17] J. M. Shapiro, "Embedded Image Coding Using Zerotrees of
Wavelet Coefficients", IEEE Trans. Signal Processing, vol. 41,
no. 12, pp. 3445-3462, Dec. 1993.
[1] S. M. S. Jalaleddine, C. G. Hutchens, R. D. Strattan, and W. A.
Coberly, "ECG data compression techniques- a unified approach,"
IEEE Trans. Biomed. Eng., vol. 37, no. 4, pp. 329-343, Apr. 1990.
[2] M. Cotronei, L. B. Montefosco , and L. Puccio, "Multiwavelet
analysis and Signal Processing," IEEE Trans. Circuit and System,
vol.45,no.8, pp. 970-987, Aug.1998
[3] V. Strela, P.N. Heller, G. Strang, P. Topiwala, C. Heil, "The
application of multiwavelet filter banks to image processing,"
IEEE Trans. Image processing, vol. 8(4), pp.548-563, April 1999
(Also Technical Report, MIT, Jan. 1996).
[4] V. Strela, Multiwavelets: Theory and Application, PhD. Thesis,
MIT, 1996.
[5] H. Soltanian-Zadeh and K. Jafari-khouzani, "Multiwavelet gradind
of prostate pathological images," Proceedings of SPIE Medical
Imaging conference, San Diego, CA, Feb. 2002.
[6] P. N. Heller, V. Strela, G. strang, P. Topiwala, C. Heil, and L. S.
Hills, "Multiwavelet filter banks for data compression," IEEE
proc. of the Int. symp. on Circuits and System, pp. 1796-1799,
1995.
[7] M. Cotronei, D. Lazzaro, L. B. Montefusco, and L. Puccio, "Image
Compression Through Embedded Multiwavelet Transform
Coding," IEEE Trans. Image Proc., vol. 9, No. 2, pp.184-189,
Feb. 2000.
[8] T. R. Dowine, and B. W. silverman, "The discrete multiple
wavelet transform and thresholding methods," Technical Report,
University of Bristol, November 1996 (also in IEEE Trans. On
Signal Processing, vol.46, pp. 2558-2561, 1998).
[9] V. Strela and A. T. Walden, "Signal and image denoising via
wavelet thresholding: Orthogonal and biorthogonal, scaler and
multiple wavelet transform," Imperial College, Statistics Section,
Technical Report, TR-98-01, 1998.
[10] T. N. T. Goodman and S. L. Lee, "Wavelet of Multiplicity r",
Trans. Amer. Math Soc., vol. 342, pp. 307-329, 1994.
[11] X. G. Xia, "A New prefilter Design for Discrete Multiwavelet
transforms," IEEE Trans. Signal Processing, vol. 46, No. 6,
pp.1558-1570, 1998.
[12] G. Plonka and V. Strela, "From wavelet to multiwavelets," Math
Methods for Curves and Surf. II., M. Dahlem, T. Lyche, L.
Shumaker (eds), Vanderblt University Press, pp.375-399, 1998.
[13] W. Selesnick, "Cardinal multiwavelets and the sampling theorem,"
Proc. Of IEEE Int. Conf. Acoustics , Speech, and Signal
Processing, vol. 3, pp. 12109-1212, 1999.
[14] X. G. Xia, J. S. Geronimo, D. P. Hardin, and B. W. Suter, "Design
of prefilters for discrete multiwavelet transforms," IEEE Trans.
Signal Processing, vol. 44, No. 1, pp.25-35, 1996.
[15] Zhito Lu, Dong Yong kim, Pearlman, W.A. "Wavelet compression
of ECG signals by the set partitioning in hierarchical trees
algorithm," IEEE Trans. Biomed. Eng., vol. 47, No. 7, pp. 849-
856, July 2000.
[16] Said, W. A. Pearlman, "A New, Fast and Efficient Image Codec
Based on Set Partitioning in Hierarchical Tress", IEEE Trans.
Circ. Sys. Vid. Tech., vol. 6, pp. 243-250, June 1996
[17] J. M. Shapiro, "Embedded Image Coding Using Zerotrees of
Wavelet Coefficients", IEEE Trans. Signal Processing, vol. 41,
no. 12, pp. 3445-3462, Dec. 1993.
@article{"International Journal of Medical, Medicine and Health Sciences:63184", author = "Morteza Moazami-Goudarzi and Ali Taheri and Mohammad Pooyan and Reza Mahboobi", title = "Multiwavelet and Biological Signal Processing", abstract = "In this paper we are to find the optimum multiwavelet for compression of electrocardiogram (ECG) signals and then, selecting it for using with SPIHT codec. At present, it is not well known which multiwavelet is the best choice for optimum compression of ECG. In this work, we examine different multiwavelets on 24 sets of ECG data with entirely different characteristics, selected from MIT-BIH database. For assessing the functionality of the different multiwavelets in compressing ECG signals, in addition to known factors such as Compression Ratio (CR), Percent Root Difference (PRD), Distortion (D), Root Mean Square Error (RMSE) in compression literature, we also employed the Cross Correlation (CC) criterion for studying the morphological relations between the reconstructed and the original ECG signal and Signal to reconstruction Noise Ratio (SNR). The simulation results show that the Cardinal Balanced Multiwavelet (cardbal2) by the means of identity (Id) prefiltering method to be the best effective transformation. After finding the most efficient multiwavelet, we apply SPIHT coding algorithm on the transformed signal by this multiwavelet.
", keywords = "ECG compression, Prefiltering, Cardinal
Balanced Multiwavelet.", volume = "2", number = "11", pages = "365-9", }
