JBiSE  Vol.6 No.7 , July 2013
Wavelet-based ECG data compression optimization with genetic algorithm
ABSTRACT

With a direct impact on compression performance, optimal quantization scheme is crucial for transform-based ECG data compression. However, traditional optimization schemes derived with signal adaption are commonly inherent with signal dependency and unsuitable for real-time application. In this paper, the variety of arrhythmia ECG signal is utilized for optimizing the quantization scheme of wavelet-based ECG data compression based on a genetic algorithm (GA). The GA search can induce a stationary relationship among the quantization scales of multi-resolution levels. The stationary property facilitates the control of multi-level quantization scales with a single variable. For this aim, a three-dimensional (3-D) curve fitting technique is applied for deriving a quantization scheme with linear distortion characteristic. The linear distortion property can be almost independent of ECG signals and provide fast error control. The compression performance and convergence speed of reconstruction quality maintenance are also evaluated by using the MIT-BIH arrhythmia database.


Cite this paper
Wu, T. , Hung, K. , Liu, J. and Liu, T. (2013) Wavelet-based ECG data compression optimization with genetic algorithm. Journal of Biomedical Science and Engineering, 6, 746-753. doi: 10.4236/jbise.2013.67092.
References
[1]   Thaler, M.S. (2012) The ONLY EKG book you’ll ever need. Lippincott Williams & Wilkins.

[2]   Ku, C.T., Hung, K.C., Wu, T.C. and Wang, H.S. (2010) Wavelet-based ECG data compression system with linear quality control scheme. IEEE Transactions on Biomedical Engineering, 57, 1399-1409. doi:10.1109/TBME.2009.2037605

[3]   Brechet, L., Lucas, M.F., Doncarli, C. and Farina, D. (2007) Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Transactions on Biomedical Engineering, 54, 2186-2192. doi:10.1109/TBME.2007.896596

[4]   Al-Fahoum, A.S. (2006) Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure. IEEE Transactions on Information Technology in Bio medicine, 10, 182-191. doi:10.1109/TITB.2005.855554

[5]   Miaou, S.G. and Lin, C.L. (2002) A quality-on-demand algorithm for wavelet-based compression of electrocar diogram signals. IEEE Transactions on Biomedical Engineering, 49, 233-239. doi:10.1109/10.983457

[6]   Zigel, Y., Cohen, A. and Katz, A. (2000) The weighted diagnostic distortion (WDD) measure for ECG signal compression. IEEE Transactions on Biomedical Engineering, 47, 1422-1430. doi:10.1109/TBME.2000.880093

[7]   Lee, S.J., Kim, J. and Lee, M. (2011) A real-time ECG data compression and transmission algorithm for an e health device. IEEE Transactions on Biomedical Engineering, 58, 2448-2455. doi:10.1109/TBME.2011.2156794

[8]   Lu, Z., Kim, D.Y. and Pearlman, W.A. (2000) Wavelet compression of ECG signals by the set partitioning in hierarchical trees (SPIHT) algorithm. IEEE Transactions on Biomedical Engineering, 47, 849-856. doi:10.1109/10.846678

[9]   Nielsen, M., Kamavuako, E.N., Andersen, M.M., Lucas M.F. and Farina, D. (2006) Optimal wavelets for bio medical signal compression. Medical and Biological Engineering and Computing, 44, 561-568. doi:10.1007/s11517-006-0062-0

[10]   He, Z. and Mitra, S.K. (2000) Optimalquantization error feedback filter for wavelet image compression. International Conference on Image Processing, 3, 166-169.

[11]   Demir, B., Erturk, S. and Urhan, O. (2009) Improved quality multiple description 3D mesh coding with optimal filtering. International Conference on Image Processing, Cairo, 7-10 November 2009, 3541-3544.

[12]   Batista, L.V., Melcher, E.U.K. and Carvalho, L.C. (2001) Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. Medical Engineering & Physics, 23, 127-134. doi:10.1016/S1350-4533(01)00030-3

[13]   Velasco, M.B., Roldan, F.C., Llorente, J.I.G. and Barner, K.E. (2004) ECG compression with retrieved quality guaranteed. Electronics Letters, 40, 1466-1467. doi:10.1049/el:20046382

[14]   Lu, Z., Kim, D.Y. and Pearlman, W.A. (2000) Wavelet compression of ECG signals by the set partitioning in hierarchical trees (SPIHT) algorithm. IEEE Transactions on Biomedical Engineering, 47, 49-856

[15]   Ku, C.-T. and Hung, K.-C. (2006) A novel ECG data compression method based on nonrecursive discrete periodized wavelet transform. IEEE Transactions on Bio medical Engineering, 53, 2577-2583. doi:10.1109/TBME.2006.881772

[16]   Moody, G.B. and Mark, R.G. (2001) The impact of the MIT/BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20, 45-50. doi:10.1109/51.932724

[17]   Holland, J.H. (1998) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. MIT Press, Cambridge.

[18]   Golciberg, D.E. (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Boston.

[19]   Michalewicz, Z. (1992) Genetic algorithms + data structures = evolution programs. Springer-Verlag, Berlin. doi:10.1007/978-3-662-02830-8

[20]   Ku, C.-T. and Hung, K.-C. (2010) Wavelet-based ECG data compression system with linear quality control scheme. IEEE Transactions on Biomedical Engineering, 57, 1399-1409. doi:10.1109/TBME.2009.2037605

 
 
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