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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)  
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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.
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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.
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Mr. Ahmed Zakaria
Assiut University - United Kingdom