Although digital storage media is not expensive and computational power has exponentially increased in the recent years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of the growing data that has to be stored or transmitted. The data’s growth depends on the factors like the sampling rate, quantization levels and number of sensors per minute per patient, depending upon the time and amplitude, sampling rate etc. Besides the increased storage capacity for archival purposes, ECG compression also allows real-time transmission over telephone networks, economic off-line transmission to remote interpretation sites etc. ECG compression methods attempt to reduce the dimensionality of the non stationary and quasi periodical ECG signal, while retaining all clinically significant features including P-wave, QRS complex and the T-wave. Wavelets have recently been emerged as powerful tools for signal compression. A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method that employs embedded zero tree (EZW) based compression algorithm is proposed in this paper. The reconstruct signal guaranteed the same RR interval as the original signal which is the major attraction presented in this paper. Records selected from the MIT-BIH arrhythmia database are tested and the experimental results show that the proposed method not only achieves high compression ratio with relatively low distortion but also is effective for various kinds of ECG morphologies.

1. Daubechies, Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 61. Philadelphia, PA: SIAM, 1992.
2. J. J. Benedetto and A. Teolis, “A wavelet auditory model and data Compression,” Appl. Computational Harmonic Anal., vol. 1, no. 1, pp. 3–28, Dec. 1993.
3. M. L. Hilton, B. D. Jawerth, and A. Sengupta, “Compressing still and moving images with wavelets,” Multimedia Syst., vol. 2, pp. 218–227,1994.
4. M. V. Wickerhauser, “Acoustic signal compression with wavelet packets,” in Wavelets: A Tutorial in Theory and Applications, vol. 2, C. K.Chui, Ed. New York: Academic, 1992, pp. 679–700.
5. J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Processing, vol. 41, no. 12, pp. 3445–3462, Dec. 1993.
6. H. Lee and K. M. Buckley, “ECG data compression using cut and align beats approach and 2-D transforms,” IEEE Trans. Biomed. Eng., vol. 46, no. 5, pp. 556–564, May 1999.
7. A. Bilgin, M. W. Marcellin, and M. I. Altbach, “Compression of electrocardiogram signals using JPEG2000,” IEEE Trans. Consum. Electron., vol. 49, no. 4, pp. 833–840, Nov. 2003.
8. S. C. Tai, C. C. Sun, and W. C. Yan, “A 2-D ECG compression method based on wavelet transform and modified SPIHT,” IEEE Trans. Biomed Eng., vol. 52, no. 6, pp. 999–1008, Jun. 2005.
9. 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.
10. 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.
11. A. Said and W. A. Pearlman, “A new fast and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. for Video Tech., vol. 6, no. 3, pp. 243–250, June 1996.
12. H. Lee and K. M. Buckley, “ECG data compression using cut and align beats approach and 2-D transform,” IEEE Trans. Biomed. Eng., vol. 46, no. 5, pp. 556–564, May 1999.
13. I. H. Witten, R. M. Neal, and J. G. Cleary, “Arithmetic coding for data compression,” Commun. ACM, vol. 30, no. 6, pp. 520–540, June 1987.
14. Pan J, Tomkins WJ:A real time QRS detection algorithm .IEEE Trans Biomedical Engg BME-32,pp 230,1985.
15. Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. “Heart Rate Variability: Standards of measurement, physiological interpretation, and clinical use”, European Heart Journal; 17; 1996; p. 354-381.
16. PhysioBank, physiologic signal archives for biomedical research (http://www.physionet.org/physiobank/).

ECG Compression EZW wavelet transform compression ratio