Compression of ECG signal using video codec technology-like scheme


In this paper, we present a method using video codec technology to compress ECG signals. This method exploits both intra-beat and inter-beat correlations of the ECG signals to achieve high compression ratios (CR) and a low percent root mean square difference (PRD). Since ECG signals have both intra-beat and inter-beat redundancies like video signals, which have both intra-frame and inter-frame correlation, video codec technology can be used for ECG compression. In order to do this, some pre-process will be needed. The ECG signals should firstly be segmented and normalized to a sequence of beat cycles with the same length, and then these beat cycles can be treated as picture frames and compressed with video codec technology. We have used records from MIT-BIH arrhythmia database to evaluate our algorithm. Results show that, besides compression efficiently, this algorithm has the advantages of resolution adjustable, random access and flexibility for irregular period and QRS false detection.

Share and Cite:

Chen, D. and Yang, S. (2008) Compression of ECG signal using video codec technology-like scheme. Journal of Biomedical Science and Engineering, 1, 22-26. doi: 10.4236/jbise.2008.11004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] 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,pp.556-565,1999.
[2] Ali Bilgin and W. Marcellin, “Compression of electrocardiogram signals using JPEG2000”, IEEE Transaction on Consumer Electronics, vol.49, NO.4, Nov. 2003.
[3] Engelse, W.A.H., Zeelenberg, C. (1979), “A single scan algorithm for QRS detection and feature extraction”, IEEE Computers in Cardiology, p37-42.
[4] J.-J. Wei, C.-J. Chang, N.-K. Chou, and G.-J. Jan, “ECG data compression using truncated singular value decomposition”, IEEE Trans. on Information Technology in Biomedicine, vol. 5, pp. 290-299,Dec. 2001.
[5] T. M. Lehman, C. Gonner, and K. Spitzer, “Survey: interpolation methods in medical image processing”, IEEE Trans. on Medical Imaging, vol. 18, pp. 1049-1075, Nov. 1999.
[6] James A. Storer, ed. “Practical implementations of arithmetic coding”, Image and text compression, MA, 1992 pages 85-112.
[7] I.Witten, R.Neal and J. Cleary, “Arithmetic coding for data compression”, Communications of the ACM, 30(6), June 1987.
[8] 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, vol. 47, pp. 849-856, July 2000.
[9] M. L. Hilton, “Wavelet and wavelet packet compression of electrocardiograms”, IEEE Trans. on Biomedical Engineering, vol. 44, pp. 394-402, May 1997.
[10] A. Djohan, T. Q. Nguyen, and W. J. Tompkins, “ECG compression using discrete symmetric wavelet transform,” in Proc. of 17th Int. IEEE Med. Biol. Conf., 1995.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.