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Ecg Compression by Modelling the Instantaneous Module/Phase of Its Dct

  • Jean-Claude Nunes
  • Amine Nait-Ali
Article

Abstract

Recent developments in compression methods on the non-linear and non-stationary data, such as electrocardiograms (ECG), have received large attention by the time-frequency analysts. The technique presented in this paper is based on parametrical modeling the instantaneous module as well as the instantaneous phase, estimated directly from the Discrete Cosine Transform (DCT) of each ECG beat. The estimated parameters are then used to reconstruct each recorded beat. In order to evaluate the performance of our technique, data recorded from the MIT-BIH arrhythmia database are used.

Key Words

Compression ECG discrete cosine transform polynomial modeling time-frequency analysis. 

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References

  1. 1.
    Ishijima M, Shin S, Hostetter G, Sklansky J. Scan-along polygonal approximation for data compression of electrocardiograms. IEEE Trans Biomed Eng 1983; 30: 723–729.PubMedGoogle Scholar
  2. 2.
    Reddy B, Murthy I. ECG data compression using Fourier descriptors. IEEE Trans Biomed Eng 1986; 33: 428–434.PubMedGoogle Scholar
  3. 3.
    Blanchett T, Kember G, Fenton G. KLT-based quality controlled compression of single lead ECG. IEEE Trans Biomed Eng 1998; 45: 942–945.CrossRefPubMedGoogle Scholar
  4. 4.
    Philips W. ECG data compression with time-warped polynomials. IEEE Trans Biomed Eng 1993; 40: 1097–1100.CrossRefGoogle Scholar
  5. 5.
    Nave G, Cohen A. ECG compression using long-term prediction. IEEE Trans Biomed Eng 1993; 40: 877–885.CrossRefPubMedGoogle Scholar
  6. 6.
    Hamilton P, Tompkins W. Compression of ambulatory ECG by average beat subtraction and residual differencing. IEEE Trans Biomed Eng 1991; 38: 253–259.CrossRefPubMedGoogle Scholar
  7. 7.
    Madhukar B, Murthy I. ECG data compression by modeling. Comput Biomed Res 1993; 26: 310–317.CrossRefPubMedGoogle Scholar
  8. 8.
    Cardenas-Barrera J, Lorenzo-Ginori J. Mean-shape vector quantizer for ECB signal compression. IEEE Trans Biomed Eng 1999; 46: 62–70.CrossRefPubMedGoogle Scholar
  9. 9.
    Hilton M. Wavelet and wavelet packet compression of electrocardiograms. IEEE Trans Biomed Eng 1997; 44: 394–402.CrossRefPubMedGoogle Scholar
  10. 10.
    Rao K, Yip P. Discrete Cosine Transform - Algorithms, Advantages, Applications. San Diego: Academic Press, 1990.Google Scholar
  11. 11.
    Lee H, Buckley K. ECG data compression using cut and align beats approach and 2D transforms. IEEE Trans Biomed Eng 1999; 46: 556–564.CrossRefPubMedGoogle Scholar
  12. 12.
    Hans M. Optimization of digital audio for internet transmission. Georgia Institute of Technology, 1998.Google Scholar
  13. 13.
    Ratnakar V. Quality-controlled lossy image compression. Madison: University of Wisconsin, 1997.Google Scholar
  14. 14.
    Wallace G. The JPEG Still Picture Compression Standard, presented at Communications of the ACM, 1991.Google Scholar
  15. 15.
    Friesen GM, Jannett TC. Comparison of noise sensitivity of nine QRS detection algorithms. IEEE Trans Biomed Eng 1990; 37(1).Google Scholar
  16. 16.
    Xue Q, Hu YH, Tompkins WJ. Neural-Network based adaptive matched filtering for QRS detection. IEEE Trans Biomed Eng 1992; 39(4).Google Scholar
  17. 17.
    Sahambi JS, Tandon SN, Bhatt RKP. Using wavelet transform for ECG characterization. An on-line digital signal processing. IEEE Eng Med Biol 1997; 16(1).Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Laboratoire d'Etude et de Recherche, en Instrumentation, Signaux et Systèmes (LERISS, EA 412)Université Paris XII-Val de MarneCréteilFrance

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