Abstract
This paper present a model to compression of ECG signals based in wavelet transform, the coefficients of expansion of the original signal are coding with the Set partitioning in Hierarchical Trees algorithm (SPIHT). The SPIHT algorithm is the last generation of coders used with wavelet transform, this algorithm is employing more sophisticated coding of images and signals. In this work we implemented a modification in the MSPIHT algorithm, to this algorithm we introduce a new modification to signal analysis in 1-D. Compression ratios of up to 24:1 for ECG signals lead to acceptable results for visual inspection and analysis by medical doctors.
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Referencias
S. Tai, C. Sun, and W. Yan, “A 2-D ECG compression method based on wavelet transform and modified SPIHT,” IEEE Transaction on Biomedical Engineering, vol 52, no. 6, 2005.
A. Said and W. Pearlman, “A new, fase, and efficient image codec based on set partitioning inhierarchical trees,” IEEETransaction on Circuits and Systems for Video Technology, vol6, no. 3, 1996.
J. Ritter, G. Fey, and P. Monitor, “SPIHT implemented in a XC4000 device,” MWSCAS, vol. 1, 2002.
Z. Lu, D. Kim, and W. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 7, 2000.
M. Wegmueller, D. Perles, T. Blazer, S. Senn, P. Stadelmann, N. Felpar, and W. Fichtner. (2006, August). On the “Silicon implementation of the SPIHT algorithm for compression of ECG records,” [on line]. Disponble en www.iis.ee.ethz.ch/~mwegmuel/ethz2/docs/SPIHT
M. Pooyan, A. Taheri, M. Moazami, I. Saboori, “Wavelet compression of ECG signal using SPIHT algorithm,” IEEE Transaction on Engineering, Computing and Technology, vol. 2, December, 2004.
S. Mallat. “A theory for multiresolution signaldecomposition: the wavelet representation,” IEEE Pattern Anal. And Machine Intell., vol. 11, no. 7. pp. 674–693, 1989.
M. Misiti, Y. Misiti, G. Openheim y J. M. Poggy. “Wavelet Toolbox, User’s Guide,” The Math work, Inc. 2000.
W.J Tompkins. Biomedical digital signal processing. C Language Examples and Laboratory Experiments. Englewood Cliffs, NJ: Prentice Hall, pp. 193, 1993.
S. Strahl, A. Mertins. “An efficient fine-grain scalable compression scheme for sparse data,” [on line]. Avaible: http://spars05.irisa.fr/ACTES/PS1-13.pdf
S. M. S. Jjalalleddine, C. G. Hutcheens, 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.
A. Djohan, T. Q. Nguyen, W. J. Tompkins, “ECG Compression Using Discrete Symetrical Wavelet Transform”, Proc. IEEE Intl. Conf. EMBS, 1995
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Mabel, R.B., Ricardo, R.D., Leonardo, R.L. (2007). Compresión de señales de ECG mediante transformada wavelet con algoritmo MSPITH-1D. In: Müller-Karger, C., Wong, S., La Cruz, A. (eds) IV Latin American Congress on Biomedical Engineering 2007, Bioengineering Solutions for Latin America Health. IFMBE Proceedings, vol 18. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74471-9_3
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DOI: https://doi.org/10.1007/978-3-540-74471-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74470-2
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