skip to main content
10.1145/1363686.1363999acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Compressing electrocardiogram signals using parameterized wavelets

Published: 16 March 2008 Publication History

Abstract

A compression method, based on the choice of a wavelet that matches the electrocardiogram signal to be compressed, is proposed in this paper. The scaling filter that minimizes the distortion of the compressed signal are used to determine the wavelet. The scaling filter is used to generate the coefficients of projection of the ECG signal in the scaling and wavelet subspaces, and only the most significant coefficients are retained. Coding methods are applied to the retained coefficients in order to improve the compression.

References

[1]
C. M. Agulhari, R. M. R. Silveira, and I. S. Bonatti. Lossless compression applied to sequences of bits. Technical report, Unicamp, Brazil, http://www.dt.fee.unicamp.br/~ ivanil/lossless_bitmap_agulhari_2007.pdf, 2007.
[2]
C. M. Agulhari, R. M. R. Silveira, and I. S. Bonatti. Lossless compression applied to wavelet coefficients. Technical report, Unicamp, Brazil, http://www.dt.fee.unicamp.br/~ ivanil/lossless significant_coefficients_agulhari_2007.pdf, 2007.
[3]
A. Alshamali and A. S. Al-Fahoum. Comments on "An efficient coding algorithm for the compression of ECG signals using the wavelet transform". IEEE Transactions on Biomedical Engineering, 50(8):1034--1037, Aug. 2003.
[4]
R. Besar, C. Eswaran, S. Sarib, and R. Simpson. On the choice of the wavelets for ECG data compression. In Proceedings of Acoustics, Speech, and Signal Processing ICASSP '00, volume 6, pages 3614--3617, June 2000.
[5]
B. Bradie. Wavelet packet-based compression of single lead ECG. IEEE Transactions on Biomedical Engineering, 43(5):493--501, May 1996.
[6]
S. D. Bradley. Optmizing a scheme for run length encoding. Proceedings of the IEEE, 57(1), Jan 1969.
[7]
J. R. Buck, M. M. Daniel, and A. C. Singer. Computer Explorations in Signals and Systems Using MATLAB®. Signal Processing. Prentice Hall, second edition, 2002.
[8]
C. S. Burrus, R. A. Gopinath, and H. Guo. Introduction to Wavelets and Wavelet Transforms -- A Primer. Prentice Hall, 1st. edition, 1998.
[9]
J. Chen and S. Itoh. A wavelet transform-based ECG compression method guaranteeing desired signal quality. IEEE Transactions on Biomedical Engineering, 45(12):1414--1419, Dec. 1998.
[10]
I. Daubechies. Ten Lectures on Wavelets. Society for Industrial amd Applied Mathematics, 8th. edition, 1992.
[11]
A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):215--220, June 2000.
[12]
S. Golomb. Run-length encoding. IEEE Transactions on Information Theory, IT-12:399--401, 1966.
[13]
Y. Hao and P. Marziliano. An efficient wavelet-based pattern matching scheme for ecg data compression. 2004 IEEE International Workshop on Biomedical Circuits and Systems, pages S2/4 -- S5--8, 2004.
[14]
M. L. Hilton. Wavelet and wavelet packet compression of electrocardiograms. IEEE Transactions on Biomedical Engineering, 44(5):394--402, May 1997.
[15]
D. A. Huffman. A method for the construction of minimum-redundancy codes. Proc. IRE, 40:1098--1101, 1952.
[16]
S. M. S. Jalaleddine, C. G. Hutchens, R. D. Strattan, and W. A. Coberly. ECG data compression techniques -- a unified approach. IEEE Transactions on Biomedical Engineering, 37:329--343, April 1990.
[17]
Z. Lu, D. Y. Kim, and W. A. Pearlman. Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Transactions on Biomedical Engineering, 47(7):849--856, July 2000.
[18]
S. G. Miaou, H. L. Yen, and C. L. Lin. Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook. IEEE Transactions on Biomedical Engineering, 49(7):671--680, July 2002.
[19]
B. A. Rajoub. An efficient coding algorithm for the compression of ECG signals using the wavelet transform. IEEE Transactions on Biomedical Engineering, 49(4):355--362, April 2002.
[20]
A. G. Ramakrishnan and S. Saha. ECG coding by wavelet-based linear prediction. IEEE Transactions on Biomedical Engineering, 44(12):1253--1261, Dec. 1997.
[21]
R. M. R. Silveira, C. M. Agulhari, I. S. Bonatti, and P. L. D. Peres. Compressão de sinais de eletrocardiogramas com wavelets determinadas por otimizaçao genética. Simpósio Brasileiro de Telecomunicacôes - SBrT 2007, 2007.
[22]
J. A. Storer. Data compression: methods and theory. Computer Science Press, Inc., New York, NY, USA, 1988.
[23]
T. A. Welch. A technique for high-performance data compression. Computer, 17(6):8--19, Jun 1984.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
March 2008
2586 pages
ISBN:9781595937537
DOI:10.1145/1363686
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. ECG
  2. data compression
  3. electrocardiogram signals
  4. signal processing
  5. wavelet

Qualifiers

  • Research-article

Conference

SAC '08
Sponsor:
SAC '08: The 2008 ACM Symposium on Applied Computing
March 16 - 20, 2008
Fortaleza, Ceara, Brazil

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 206
    Total Downloads
  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media