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ECG Signal Compression Technique Based on Discrete Wavelet Transform and QRS-Complex Estimation
Ahmed Zakaria
Pages - 138 - 160     |    Revised - 30-6-2010     |    Published - 10-08-2010
Volume - 4   Issue - 3    |    Publication Date - July 2010  Table of Contents
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KEYWORDS
,MIMO-OFDM system, DWDM system , Space Time Coding, BER,
ABSTRACT
In this paper, an Electrocardiogram (ECG) signal is compressed based on discrete wavelet transform (DWT) and QRS-complex estimation. The ECG signal is preprocessed by normalization and mean removal. Then, an error signal is formed as the difference between the preprocessed ECG signal and the estimated QRS-complex waveform. This error signal is wavelet transformed and the resulting wavelet coefficients are thresholded by setting to zero all coefficients that are smaller than certain threshold levels. The threshold levels of all subbands are calculated based on Energy Packing Efficiency (EPE) such that minimum percentage root mean square difference (PRD) and maximum compression ratio (CR) are obtained. The resulted thresholded DWT coefficients are coded using the coding technique given in [1], [20]. The compression algorithm was implemented and tested upon records selected from the MIT - BIH arrhythmia database [2]. Simulation results show that the proposed algorithm leads to high CR associated with low distortion level relative to previously reported compression algorithms [1], [14] and [18]. For example, the compression of record 100 using the proposed algorithm yields to CR = 25.15 associated with PRD = 0.7% and PSNR = 45 dB. This achieves compression rate of nearly 128 bit/sec. The main features of this compression algorithm are the high efficiency, high speed and simplicity in design.
CITED BY (17)  
1 Kharate, P. S., Raghorte, R. D., Gabhane, S. B., Khasare, S. R., Nikhar, K. D., Chauke, P., & Mishra, M. N. ECG Signal Compression Technique based on DWT & QRS Complex Estimation.
2 Surekha, K. S., & Patil, B. P. (2015). Compression of ECG Signal Using Hybrid Technique. In Intelligent Systems in Science and Information 2014 (pp. 385-396). Springer International Publishing.
3 SAHOO, G. K. (2015). A Framework for Remote Patient Monitoring to Diagnose the Cardiac Disorders (Doctoral dissertation, National Institute of Technology Rourkela).
4 Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Al-Ajlouni, A. F. (2015). Wavelet Threshold-Based ECG Data Compression Technique Using Immune Optimization Algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(2), 347-360.
5 Patwari, A. K., Pansari, A. P. D., & Singh, V. P. A Survey of ECG Signal Compression Techniques based on Discrete Wavelet Transform.
6 Patwari, A. K., Pansari, A. P. D., & Singh, V. P. Analysis of ECG Signal Compression Technique Using Discrete Wavelet Transform for Different Wavelets. signal, 5, 7.
7 Kaushik, G., Sinha, H. P., & Dewan, L. (2014).Biomedical signals analysis by dwt signal denoising with neural networks. journal of theoretical and applied information technology, 62(1).
8 Haddadi, R., Abdelmounim, E., & Belaguid, A. (2014, April). Discrete Wavelet Transform based algorithm for recognition of QRS complexes. In Multimedia Computing and Systems (ICMCS), 2014 International Conference on (pp. 375-379). IEEE.
9 Abdelmounim, E., Haddadi, R., & Belaguid, A. (2014, April). ElectroCardioGram signal denoising using Discrete Wavelet Transform. In Multimedia Computing and Systems (ICMCS), 2014 International Conference on (pp. 1065-1070). IEEE.
10 Abdelmounim, E., Haddadi, R., & Belaguid, A. (2014, November). A new simple and efficient technique for ECG compression based on leads converter and DWT coefficients thresholding. In Complex Systems (WCCS), 2014 Second World Conference on (pp. 638-643). IEEE.
11 Surekha, K. S., & Patil, B. P. (2014, August). ECG signal compression using hybrid 1D and 2D wavelet transform. In Science and Information Conference (SAI), 2014 (pp. 468-472). IEEE.
12 El hanine, m., abdelmounim, e., haddadi, r., & belaguid, a. (2014).Electrocardiogram Signal Denoising Using Discrete Wavelet Transform. Computer Technology and Application, 5(2).
13 Abo-Zahhad, M. M., Abdel-Hamid, T. K., & Mohamed, A. M. (2014). Compression of ECG signals based on DWT and exploiting the correlation between ECG signal samples. International Journal of Communications, Network and System Sciences, 7(1), 53.
14 Patwari, A. K., Pansari, D., Singh, V. P., & Singh, V. P. Nav view search.
15 Nassiri, B., Latif, R., Toumanari, A., Elouaham, S., & Maoulainine, F. M. R. (2013). ECG Signal De-Noising and Compression Using Discrete Wavelet Transform and Empirical Mode Decomposition Techniques. International Journal on Numerical and Analytical Methods in Engineering (IRENA), 1(5), 245-252.
16 Su, S. (2011). Asynchronous Signal Processing for Compressive Data Transmission (Doctoral dissertation, University of Pittsburgh).
17 Jin, Y., Lakshminarasimhan, S., Shah, N., Gong, Z., Chang, C. S., Chen, J., ... & Samatova, N. F. (2011, December). S-preconditioner for multi-fold data reduction with guaranteed user-controlled accuracy. In Data Mining (ICDM), 2011 IEEE 11th International Conference on (pp. 290-299). IEEE.
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1 B. A. Rajoub, “An efficient coding algorithm for the compression of ECG signals using the wavelet transform,” IEEE Transactions on Biomedical Engineering, 49 (4): 355–362, 2002.
2 MIT-BIH Arrhythmia Database, www.physionet.org/physiobank/database/mitdb.
3 J. Cox, F. Nulle, H. Fozzard, and G. Oliver, “AZTEC, a preprocessing program for real-time ECG rhythm analysis,” IEEE. Trans. Biomedical Eng., BME-15: 128–129, 1968.
4 R.N. Horspool and W.J. Windels, “ECG compression using Ziv-Lempel techniques, Comput” Biomed. Res., 28: 67–86, 1995.
5 B. R. S. Reddy and I. S. N. Murthy, “ECG data compression using Fourier descriptors,” IEEE Trans. Biomed. Eng., BME-33 (4): 428–434, 1986.
6 H. A. M. Al-Nashash, “ECG data compression using adaptive Fourier coefficients estimation,” Med. Eng. Phys., 16: 62–66, 1994.
7 S. C. Tai, “Improving the performance of electrocardiogram sub-band coder by extensive Markov system,” Med. Biol. Eng. And Computers, 33: 471–475, 1995.
8 J. Chen, S. Itoh, and T. Hashimoto, “ECG data compression by using wavelet transform,” IEICE Trans. Inform. Syst., E76-D (12): 1454–1461, 1993.
9 A. Cohen, P. M. Poluta, and R. Scott-Millar, “Compression of ECG signals using vector quantization,” in Proc. IEEE-90 S. A. Symp. Commun. Signal Processing COMSIG-90, Johannesburg, South Africa, pp. 45–54, 1990.
10 G. Nave and A. Cohen, “ECG compression using long-term prediction,” IEEE. Trans. Biomed. Eng., 40: 877–885, 1993.
11 A. Iwata, Y. Nagasaka, and N. Suzumura, “Data compression of the ECG using neural network for digital Holter monitor,” IEEE Eng. Med. Biol., Mag, pp. 53–57, 1990.
12 M. Abo-Zahhad, S. M. Ahmed, and A. Al-Shrouf, “Electrocardiogram data compression algorithm based on the linear prediction of the wavelet coefficients," in Proc.7th IEEE Int. Conf., Electronics, Circuits and Systems, vol. 1, Lebanon, pp. 599–603, 2000.
13 O. O. Khalifa, S. H. Harding, A. A. Hashim, “Compression Using Wavelet Transform” Signal Processing: An International Journal (SPIJ), pp. 17 – 26, 2008.
14 M. Zia Ur Rahman , R. A. Shaik, D V Rama Koti Reddy, “Noise Cancellation in ECG Signals using Computationally Simplified Adaptive Filtering Techniques: Application to Biotelemetry” Signal Processing: An International Journal (SPIJ), pp. 120 – 131, 2009.
15 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, 47(7): 849–856, 2000.
16 Shen-Chuan Tai, Chia-Chun Sun, and Wen-Chien Yan, “A 2-D ECG Compression Method Based on Wavelet Transform and Modified SPIHT.” IEEE Transactions on Biomedical Engineering, 52 (6), 2005.
17 M. Okada, “A digital filter for the QRS complex detection,”IEEET trans. Biomed. Eng., BME-26 (12): 700–703, 1979.
18 Thakor, N. V., Webster, J. G., and Tompkins, W. J., Optimal QRS detector. Medical and Biological Engineering, pp. 343–50, 1983.
19 Yaniv Zigel , Arnon Cohen, and Amos Katz,” ECG Signal Compression Using Analysis by Synthesis Coding”, IEEE Transactions on Biomedical Engineering, 47 (10), 2000.
20 Y. Zigel, A. Cohen, and A. Katz, “The weighted diagnostic distortion measure for ECG signal compression,” IEEE Trans. Biomed. Eng., 2000.
21 Abo-Zahhad, M. and Rajoub, B.A., An effective coding technique for the compression of onedimensional signals using wavelet transform. Med. Eng. Phys. 24: 185-199, 2001.
22 Ahmed, S.M., Al-Zoubi, Q. and Abo-Zahhad, M., "A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform," J. Med. Eng. Technology 31: 54-61, 2007.
23 S.M. Ahmed, A. Al-Shrouf and M. Abo-Zahhad, "ECG data compression using optimal nonorthogonal wavelet transform," Medical Engineering & Physics, 22 (1): 39-46, 2000.
24 R. Javaid, R. Besar, F. S. Abas, “Performance Evaluation of Percent Root Mean Square Difference for ECG Signals Compression” Signal Processing: An International Journal (SPIJ): 1–9, 2008.
Mr. Ahmed Zakaria
Assiut University - United Kingdom