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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
,MIMO-OFDM system, DWDM system , Space Time Coding, BER,
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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Mr. Ahmed Zakaria
Assiut University - Egypt

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