Quality-Controlled Compression Method using Wavelet Transform for Electrocardiogram Signals
This paper presents a new Quality-Controlled, wavelet based, compression method for electrocardiogram (ECG) signals. Initially, an ECG signal is decomposed using the wavelet transform. Then, the resulting coefficients are iteratively thresholded to guarantee that a predefined goal percent root mean square difference (GPRD) is matched within tolerable boundaries. The quantization strategy of extracted non-zero wavelet coefficients (NZWC), according to the combination of RLE, HUFFMAN and arithmetic encoding of the NZWC and a resulting look up table, allow the accomplishment of high compression ratios with good quality reconstructed signals.
<p>[1] N. V. Thakor, Y.-C. Sun et al., “Multiwave: a novel wavelet-based ECG data compression algorithm," IEICE Trans. On Information and Systems. Vol. E76-D, pp. 1462-1469, 1993.
[2] K. Uyar, Y. Z. Ider, “Development of compression algorithm suitable for exercise ECG data," in Proc. IEEE Int. Conf. EMBS., Turkey, pp. 3521- 3524, 2001. [3] E. Betri et al, “ECG data compression using double logarithmic quantisation of Walsh spectrum," IEE Electronics letters, vol. 31, pp. 1025-1026, 1995. [4] S-G. Miaou, C-L. Lin , “A Quality-on-demand algorithm for waveletbased compression of electrocardiogram signals," IEEE Trans. on Biomedical Engineering, vol. 49, pp. 233-239, 2002.
[5] Z. Lu, D. Y. Kim and W. A. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm," IEEE Trans. on Biomedical Engineering, vol. 47 , pp. 849-856, 2000.
[6] J. Chen, S. Itoh, “A wavelet transform-based ECG compression method guaranteeing desired signal quality," IEEE Trans. on Biomedical Engineering, vol. 45, pp.1414-1419, 1998.
[7] T. Blanchett, G. C. Kember and G. A. Fenton, “KLT-based quality controlled compression of single-lead ECG," IEEE Trans. on Biomedical Engineering, vol. 45, pp. 942-945,1998.
[8] 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.
[9] 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..
[10] A. Bilgin, M. W. Marcellin and M. I. Altbach, “Compression of electrocardiogram signals using JPEG 2000," IEEE Trans. on Consumer Electronics, vol. 49, pp.833-840, 2003.
[11] R. S. H Istepanian, L. J Hadjileontiadis.and S. M. Panas, “ECG data compression using wavelets and higher order statistics methods," IEEE Trans. on Information Technology in Biomedicine, vol. 5, pp. 108-115, 2001.
[12] S-G. Miaou and H-L. Yen, “Quality driven gold washing adaptive vector quantization and its application to ECG data compression," IEEE Trans. on Biomedical Engineering, vol. 47, pp. 209-218, 2000.
[13] A. C. D-Ambrosio, A. Ortiz-Conde and F. J. Garcia Sanchez, “Percentage Area difference (PAD) as a measure of distortion and its use in maximum enclosed area (MEA), a new ECG signal compression algorithm," in Proc. IEEE Int. Conf. on Dev., Circuits, sys., Caracas, 1035- 1-5, 2002.
[14] S-G Miaou and J.-H. Larn, “Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted codevectors," Medical & Biological Engineering & Computing, vol. 38, pp. 547-552, 2000.
[15] S. -G. Miaou, “Compression of physiological quasi-periodic signals using optimal codebook replenishment vector quantization with distortion constraint," IEICE Trans. On Information and Systems, vol. E85-D, pp. 1325-1333, 2002.
[16] R. Benzid, F. Marir et al, “Fixed percentage of wavelet coefficients to be zeroed for ECG compression," IEE Electronics Letters, vol. 39, pp. 830- 831, 2003.
[17] A. Alshamali and A. S. Al-Fahoum, “Comments on an efficient coding algorithm for the compression of ECG signals using the wavelet transform," IEEE Trans. on Biomedical Engineering, vol. 50, pp. 1034- 1037, 2003.
[18] Y. Nagasaka and A. Iwata, “Data compression of long time ECG recording using BP and PCA neural networks," IEICE Trans. on Information & Systems, vol. E-76-D, pp. 434-1442, 1993.
[19] A. G. Ramakrishnan and S. Saha, “ECG compression by multirate processing of beats," Computers and Biomedical Research, vol. 29, pp. 407-417, 1996.
[20] Y. Zigel, A. Cohen and A. Katz, “The weighted diagnostic distortion (WDD) measure for ECG signals compression," IEEE Trans. on Biomedical Engineering, vol. 47, pp. 1422-1430, 2000.
[21] W. H Press, B. P. Flannery et al, Numerical recipes in C- The art of the scientific computing, Cambridge University Press, 1992.
[22] S. P. Lloyd, “Least Squares Quantization in PCM," IEEE Transactions on Information Theory, vol. 28, pp. 129-137, 1982.
[23] J.-J.Wei , C.-J. Chang, N.-K Chou and G.-J Jan, “ECG data compression using truncated singular value decomposition," IEEE Tans. On Information Technology in Biomedicine, vol. 5, pp. 290-299, 2001.
[24] R. S. H. Istepanian and A. Petrosian, “Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system," IEEE Trans. on Information Technology in Biomedicine, vol. 4, pp.200-211, 2000.</p>
<p>[1] N. V. Thakor, Y.-C. Sun et al., “Multiwave: a novel wavelet-based ECG data compression algorithm," IEICE Trans. On Information and Systems. Vol. E76-D, pp. 1462-1469, 1993.
[2] K. Uyar, Y. Z. Ider, “Development of compression algorithm suitable for exercise ECG data," in Proc. IEEE Int. Conf. EMBS., Turkey, pp. 3521- 3524, 2001. [3] E. Betri et al, “ECG data compression using double logarithmic quantisation of Walsh spectrum," IEE Electronics letters, vol. 31, pp. 1025-1026, 1995. [4] S-G. Miaou, C-L. Lin , “A Quality-on-demand algorithm for waveletbased compression of electrocardiogram signals," IEEE Trans. on Biomedical Engineering, vol. 49, pp. 233-239, 2002.
[5] Z. Lu, D. Y. Kim and W. A. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm," IEEE Trans. on Biomedical Engineering, vol. 47 , pp. 849-856, 2000.
[6] J. Chen, S. Itoh, “A wavelet transform-based ECG compression method guaranteeing desired signal quality," IEEE Trans. on Biomedical Engineering, vol. 45, pp.1414-1419, 1998.
[7] T. Blanchett, G. C. Kember and G. A. Fenton, “KLT-based quality controlled compression of single-lead ECG," IEEE Trans. on Biomedical Engineering, vol. 45, pp. 942-945,1998.
[8] 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.
[9] 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..
[10] A. Bilgin, M. W. Marcellin and M. I. Altbach, “Compression of electrocardiogram signals using JPEG 2000," IEEE Trans. on Consumer Electronics, vol. 49, pp.833-840, 2003.
[11] R. S. H Istepanian, L. J Hadjileontiadis.and S. M. Panas, “ECG data compression using wavelets and higher order statistics methods," IEEE Trans. on Information Technology in Biomedicine, vol. 5, pp. 108-115, 2001.
[12] S-G. Miaou and H-L. Yen, “Quality driven gold washing adaptive vector quantization and its application to ECG data compression," IEEE Trans. on Biomedical Engineering, vol. 47, pp. 209-218, 2000.
[13] A. C. D-Ambrosio, A. Ortiz-Conde and F. J. Garcia Sanchez, “Percentage Area difference (PAD) as a measure of distortion and its use in maximum enclosed area (MEA), a new ECG signal compression algorithm," in Proc. IEEE Int. Conf. on Dev., Circuits, sys., Caracas, 1035- 1-5, 2002.
[14] S-G Miaou and J.-H. Larn, “Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted codevectors," Medical & Biological Engineering & Computing, vol. 38, pp. 547-552, 2000.
[15] S. -G. Miaou, “Compression of physiological quasi-periodic signals using optimal codebook replenishment vector quantization with distortion constraint," IEICE Trans. On Information and Systems, vol. E85-D, pp. 1325-1333, 2002.
[16] R. Benzid, F. Marir et al, “Fixed percentage of wavelet coefficients to be zeroed for ECG compression," IEE Electronics Letters, vol. 39, pp. 830- 831, 2003.
[17] A. Alshamali and A. S. Al-Fahoum, “Comments on an efficient coding algorithm for the compression of ECG signals using the wavelet transform," IEEE Trans. on Biomedical Engineering, vol. 50, pp. 1034- 1037, 2003.
[18] Y. Nagasaka and A. Iwata, “Data compression of long time ECG recording using BP and PCA neural networks," IEICE Trans. on Information & Systems, vol. E-76-D, pp. 434-1442, 1993.
[19] A. G. Ramakrishnan and S. Saha, “ECG compression by multirate processing of beats," Computers and Biomedical Research, vol. 29, pp. 407-417, 1996.
[20] Y. Zigel, A. Cohen and A. Katz, “The weighted diagnostic distortion (WDD) measure for ECG signals compression," IEEE Trans. on Biomedical Engineering, vol. 47, pp. 1422-1430, 2000.
[21] W. H Press, B. P. Flannery et al, Numerical recipes in C- The art of the scientific computing, Cambridge University Press, 1992.
[22] S. P. Lloyd, “Least Squares Quantization in PCM," IEEE Transactions on Information Theory, vol. 28, pp. 129-137, 1982.
[23] J.-J.Wei , C.-J. Chang, N.-K Chou and G.-J Jan, “ECG data compression using truncated singular value decomposition," IEEE Tans. On Information Technology in Biomedicine, vol. 5, pp. 290-299, 2001.
[24] R. S. H. Istepanian and A. Petrosian, “Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system," IEEE Trans. on Information Technology in Biomedicine, vol. 4, pp.200-211, 2000.</p>
@article{"International Journal of Medical, Medicine and Health Sciences:64878", author = "Redha Benzid and Farid Marir and Nour-Eddine Bouguechal", title = " Quality-Controlled Compression Method using Wavelet Transform for Electrocardiogram Signals ", abstract = "This paper presents a new Quality-Controlled, wavelet based, compression method for electrocardiogram (ECG) signals. Initially, an ECG signal is decomposed using the wavelet transform. Then, the resulting coefficients are iteratively thresholded to guarantee that a predefined goal percent root mean square difference (GPRD) is matched within tolerable boundaries. The quantization strategy of extracted non-zero wavelet coefficients (NZWC), according to the combination of RLE, HUFFMAN and arithmetic encoding of the NZWC and a resulting look up table, allow the accomplishment of high compression ratios with good quality reconstructed signals.
", keywords = "ECG compression, Non-uniform Max-Lloydquantizer, PRD, Quality-Controlled, Wavelet transform", volume = "1", number = "2", pages = "148-6", }
