Open Access

A Simple Method for Guaranteeing ECG Quality in Real-Time Wavelet Lossy Coding

EURASIP Journal on Advances in Signal Processing20072007:093195

https://doi.org/10.1155/2007/93195

Received: 3 May 2006

Accepted: 17 December 2006

Published: 21 February 2007

Abstract

Guaranteeing ECG signal quality in wavelet lossy compression methods is essential for clinical acceptability of reconstructed signals. In this paper, we present a simple and efficient method for guaranteeing reconstruction quality measured using the new distortion index wavelet weighted PRD (WWPRD), which reflects in a more accurate way the real clinical distortion of the compressed signal. The method is based on the wavelet transform and its subsequent coding using the set partitioning in hierarchical trees (SPIHT) algorithm. By thresholding the WWPRD in the wavelet transform domain, a very precise reconstruction error can be achieved thus enabling to obtain clinically useful reconstructed signals. Because of its computational efficiency, the method is suitable to work in a real-time operation, thus being very useful for real-time telecardiology systems. The method is extensively tested using two different ECG databases. Results led to an excellent conclusion: the method controls the quality in a very accurate way not only in mean value but also with a low-standard deviation. The effects of ECG baseline wandering as well as noise in compression are also discussed. Baseline wandering provokes negative effects when using WWPRD index to guarantee quality because this index is normalized by the signal energy. Therefore, it is better to remove it before compression. On the other hand, noise causes an increase in signal energy provoking an artificial increase of the coded signal bit rate. Clinical validation by cardiologists showed that a WWPRD value of 10 preserves the signal quality and thus they recommend this value to be used in the compression system.

[123456789101112131415]

Authors’ Affiliations

(1)
Communications Technology Group, Aragón Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain

References

  1. Jalaleddine SMS, Hutchens CG, Strattan RD, Coberly WA: ECG data compression techniques—a unified approach. IEEE Transactions on Biomedical Engineering 1990,37(4):329-343. 10.1109/10.52340View ArticleGoogle Scholar
  2. Cox JR, Nolle FM, Fozzard HA, Oliver GC Jr.: AZTEC: a preprocessing program for real-time ECG rhythm analysis. IEEE Transactions on Biomedical Engineering 1968,15(2):128-129.View ArticleGoogle Scholar
  3. Abenstein JP, Tompkins WJ: A new data-reduction algorithm for real-time ECG analysis. IEEE Transactions on Biomedical Engineering 1982,29(1):43-48.View ArticleGoogle Scholar
  4. Olmos S, Millán M, García J, Laguna P: ECG data compression with the Karhunen-Loève transform. Proceedings of Computers in Cardiology, September 1996, Indianapolis, Ind, USA 253-256.Google Scholar
  5. Ahmed N, Milne PJ, Harris SG: Electrocardiographic data compression via orthogonal transforms. IEEE Transactions on Biomedical Engineering 1975,22(6):484-487.View ArticleGoogle Scholar
  6. Lu Z, Kim DY, Pearlman WA: Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Transactions on Biomedical Engineering 2000,47(7):849-856. 10.1109/10.846678View ArticleGoogle Scholar
  7. Hilton ML: Wavelet and wavelet packet compression of electrocardiograms. IEEE Transactions on Biomedical Engineering 1997,44(5):394-402. 10.1109/10.568915View ArticleGoogle Scholar
  8. Chui C: An Introduction to Wavelets. Academic Press, London, UK; 1992.MATHGoogle Scholar
  9. Al-Fahoum AS: Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure. IEEE Transactions on Information Technology in Biomedicine 2006,10(1):182-191. 10.1109/TITB.2005.855554View ArticleGoogle Scholar
  10. Miaou S-G, Lin C-L: A quality-on-demand algorithm for wavelet-based compression of electrocardiogram signals. IEEE Transactions on Biomedical Engineering 2002,49(3):233-239. 10.1109/10.983457View ArticleGoogle Scholar
  11. Said A, Pearlman WA: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 1996,6(3):243-250. 10.1109/76.499834View ArticleGoogle Scholar
  12. Moody GB, Mark RG: The MIT-BIH arrhythmia database on CD-ROM and software for use with it. Proceedings of Computers in Cardiology, September 1990, Chicago, Ill, USA 185-188.Google Scholar
  13. Moody GB, Mark RG, Goldberger AL: Evaluation of the 'TRIM' ECG data compressor. Proceedings of Computers in Cardiology, September 1988, Washington, DC, USA 167-170.Google Scholar
  14. Sörnmo L, Laguna P: Biomedical Signal Processing in Cardiac and Neurological Applications. Elsevier, San Diego, Calif, USA; 2005.Google Scholar
  15. Alesanco Á, Olmos S, Istepanian RSH, García J: Enhanced real-time ECG coder for packetized telecardiology applications. IEEE Transactions on Information Technology in Biomedicine 2006,10(2):229-236. 10.1109/TITB.2005.856853View ArticleGoogle Scholar

Copyright

© Á. Alesanco and J. García. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.