Wavelet Based ECG Signal Coding Using Burrows-Wheeler Transformation, Move-To Front Coder and Arithmetic Coder
In this paper electrocardiogram (ECG) signal is coded using Wavelet based Burrows-Wheeler Transformation, Move to Front Coder and Arithmetic coder. Discrete Wavelet Transform (DWT) is applied to decompose the ECG signal. Then Burrows-Wheeler Transformation (BWT), Move to front coder (MTF) and Arithmetic coder are used to compress the decomposed ECG signal. Compression Ratio (CR) and Percent Root mean square nDifference (PRD) are used asperformance measures. ECG signals / records from MIT BIH arrhythmia database are used to evaluate the performance of this coder. This coder is tested with twenty five different records from MIT BIH arrhythmia data base and obtained the average PRD as 0.0306% to 4.3875% for the average CR of 2.528:1 to 33.8206:1. For record 117 the CR of 8.5317:1 is achieved with PRD 1.0836%. This experimental results show that this coder out performs than other coders such as Djohn, EZW, SPIHT, Novel algorithm etc exists in the literature in terms of coding efficiency and in computation.
S.M.S. Jalaleddine, C.G.Hutchens, R.D.Strattan and W.A.Coberly, “EGC data compression techniques- A unified approach”, IEEE Trans. Biomed. Eng., Vol. 37. no. 4, pp. 329 – 343, April 1990.
Philips, “ECG data compression with time – warped polynomials”,IEEE.Trans.Biomed. Eng. , vol. 40, no. 11, pp. 1095 –1101, Nov. 1993.
P.S.Hamilton and W.J.Tompkins, “Compression of the ambulatory ECG by average beat subtraction and residual differencing”, IEEE.Trans.Biomed. Eng., vol.38, no.3, pp. 253 – 259, Mar. 1991.
A.E. Cetin, H. Koymen and M.C.Aydin, “ECG data compression by sub band coding,” Electron. Lett., vol.27, pp.359-360.Feb.1991.
A.Djohn, T.Q.Nguyen and W.J.Tompkins, “ECG Compression using discrete symmetric wavelet transform,”presented at the 17th IEEE Int.Conf. Medicine and Biology, Montreal, QC.Canada, 1995.
A.G.Ramakrishnan, Supratim Saha, “ECG coding by wavelet based linear prediction,” IEEE.Trans. Biomed. Eng., vol.44, pp. 1253 – 1261, Dec.1997.
Michael L. Hilton, “Wavelet and wavelet packet compression of Electocardiogram,”, IEEE Trans. Biomed. Eng., vol.44, no.5, pp.394-402, May1997.
Z. Lu, D.Y.Kim, and W.A. Pearlman, “Wavelet compression of ECG signals by set partitioning in hierarchical trees (SPIHT) algorithm,”IEEE.Trans. Biomed. Eng., vol.47, no.5, pp. 849 – 856, July.2000.
R.Shantha Selva Kumari and V.Sadasivam,” A Novel Algorithm for Wavelet Based ECG Signal Coding” Computers and Electrical Engineering, vol.33 pp.186-194, Feburary 2007.
J.D.Villasenor,B.Belzer, and J.Liao, “Wavelet filter evaluation for image compression,” IEEE.Trans. Image Processing. vol.4, no.8, pp.1053-1060,Aug. 1995.
Ziya Arnaut ECG Signal Compression Based on Burrows-Wheeler Transformation and Inversion Ranks of Linear Prediction, IEEE Trans.Biomed. Eng., vol. 54, no:3, March 2007.
S.G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation”, IEEE.Trans. Pattern Anal.Machine Intell.,vol.11, no.7, pp. 674 – 693, JULY 1989.
S.M. Phoong, C.W.Kim, P.P.Vaidyanathan and R.Ansari, “A new class of two channel biorthogonal filter banks and wavelet bases”, IEEE.Trans.Signal Processing. vol.43, no.12, pp. 649 – 665, Mar.1995.
P. Yip and K.R.Rao, “Energy packing efficiency for the generalized discrete transforms,IEEETrans.Commun” vol.26, no.8,pp.1257-1262, Aug.1978.
A.E. Cetin, H. Koymen and M.C.Aydin, “Multichannel ECG data compression by multi rate signal processing and transform coding techniques”, IEEE.Trans. Biomed. Eng., vol.40, no.5, pp. 495 – 499, May.1995.
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