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Robust Estimation of Respiratory Rate Based on Linear Regression

  • J. Łęski
  • N. Henzel
  • A. Momot
  • M. Momot
  • J. Śledzik
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

One of the basic parameters which are of very importance in patients monitoring is respiratory rate (frequency of breathing). Previous studies show that there is possibility of estimating the respiratory rate from ECG signal using the modulation amplitude of this signal during the breath. This paper presents the method which is highly sensitive to the ECG amplitude changes and also robust to disturbances, usually accompanying the signal. The idea which distinguishes the method from other is to use changes of ECG amplitude in consecutive heart cycles on the basis of the entire QRS complex. The changes are determined by robust regression analysis (which leads to iterative algorithm) and the regression coefficients are transformed to the frequency domain.

Keywords

Differential Quadrature Impulsive Noise Unbiased Minimum Variance Unbiased Minimum Variance Estimation International Electrotechnical Commission Standard 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    van Alste, J.A., van Eck, W., Herrmann, O.E.: ECG Baseline Wander Reduction Using Linear Phase Filters. Compt. Biomed. Res. 19, 417–427 (1986)CrossRefGoogle Scholar
  2. 2.
    Bailón, R., Sornmo, L., Laguna, P.: A Robust Method for ECG-Based Estimation of the Respiratory Frequency During Stress Testing. IEEE Trans. Bio. Eng. 53(7), 1273–1285 (2006)CrossRefGoogle Scholar
  3. 3.
    Braci, M., Diop, S.: On numerical differentiation algorithms for nonlinear estimation. In: Proc. 42nd IEEE Conf. on Decision and Control, Maui, Hawaii, USA, pp. 2896–2901 (2003)Google Scholar
  4. 4.
    Burden, R.L., Faires, J.D.: Numerical Analysis. Brooks/Cole, Monterey (2000)Google Scholar
  5. 5.
    Canan, S., Ozbay, Y., Karlik, B.: A Method for Removing Low Frequency Trend From ECG Signal. In: Proc. of Int. Conf. Biomed. Engin. Days, pp.144–146 (1998)Google Scholar
  6. 6.
    Chazal, P., Heneghan, C., Sheridan, E., Reilly, R., Nolan, P., O’Malley, M.: Automated processing of single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans. Bio. Eng. 50(6), 686–696 (2003)CrossRefGoogle Scholar
  7. 7.
    Ciarlini, P., Barone, P.: A Recursive Algorithm to Compute the Baseline Drift in Recorded Biological Signals. Compt. Biomed. Res. 21, 221–226 (1988)CrossRefGoogle Scholar
  8. 8.
    Correa, L.S., Laciar, E.T., Jane, R.: Performance evaluation of three methods for respiratory signal estimation from the electrocardiogram. In: 30th Ann. Int. Conf. IEEE/EMBS, pp. 4760–4763 (2008)Google Scholar
  9. 9.
    Frankiewicz, Z.: Methods for ECG signal analysis in the presence of noise. Ph.D. Thesis, Silesian Technical University, Gliwice (1987)Google Scholar
  10. 10.
    Huber, P.J.: Robust statistics. Wiley, New York (1981)CrossRefzbMATHGoogle Scholar
  11. 11.
    International Electrotechnical Commission Standard 60601-3-2 (1999)Google Scholar
  12. 12.
    Laguna, P., Jane, R., Caminal, P.: Adaptive Filtering of ECG Baseline Wander. In: Proc. of Int. Conf. of the IEEE Eng. in Med. and Biol. Soc., pp. 508–509 (1992)Google Scholar
  13. 13.
    Langley, P., Bowers, E.J., Murray, A.: Principal component analysis as a tool for analysing beat-to-beat changes in electrocardiogram features: application to electrocardiogram derived respiration. IEEE Trans. Bio. Eng. 57(4), 821–829 (2010)CrossRefGoogle Scholar
  14. 14.
    Leski, J.M.: A new possibility of non-invasive electrocardiological diagnosis. Report no.1233, Silesian University of Technology, Gliwice (1994)Google Scholar
  15. 15.
    Leski, J.M., Henzel, N.: Generalized ordered linear regression with regularization. Submitted to IEEE Trans. Syst., Man and Cybern. (2010)Google Scholar
  16. 16.
    Meyer, C.R., Keiser, H.N.: Electrocardiogram Baseline Noise Estimation and Removal Using Cubic Splines and State-Space Computation Techniques. Compt. Biomed. Res. 10, 459–470 (1977)CrossRefGoogle Scholar
  17. 17.
    Mitra, S.K., Kaiser, J.F. (eds.): Handbook for digital signal processing. John Wiley & Sons, New York (1993)zbMATHGoogle Scholar
  18. 18.
    Oguz, S.H., Asyali, M.H.: A Morphology Based Algorithm for Baseline Wander Elimination in ECG Records. In: Proc. of Int. Conf. Biomed. Engin. Days, pp. 157–160 (1992)Google Scholar
  19. 19.
    Pan, J., Tompkins, W.J.: A Real-Time QRS Detection Algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)CrossRefGoogle Scholar
  20. 20.
    Riccio, M.L., Belina, J.C.: A Versatile Design method of Fast, Linear-Phase FIR Filtering Systems for Electrocardiogram Acquisition and Analysis Systems. In: Proc. of Int. Conf. Comput. Cardiol., pp. 147–150 (1992)Google Scholar
  21. 21.
    Thakor, N.V., Zhu, Y.S.: Application of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991)CrossRefGoogle Scholar
  22. 22.
    Varanini, M., Emdin, M., Allegri, F., Raciti, M., Conforti, F., Macerata, A., Taddei, A., Francesconi, R., Kraft, G., Abbate, A.L., Marchesi, C.: Adaptive filtering of ECG signal for deriving respiratory activity. Comp. Cardiol., 621–624 (1990)Google Scholar
  23. 23.
    Yi, W.J., Park, K.S.: Derivation of respiration from ECG measured without subject apos’s awareness using wavelet transform. In: 24th Ann. Conf. EMBS/BMES, pp. 130–131 (2002)Google Scholar
  24. 24.
    Zhang, Y.: Advanced Differential Quadrature Methods. CRC Press, Boca Raton (2009)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • J. Łęski
    • 1
    • 3
  • N. Henzel
    • 1
    • 3
  • A. Momot
    • 2
  • M. Momot
    • 3
  • J. Śledzik
    • 3
  1. 1.Institute of ElectronicsSilesian University of TechnologyPoland
  2. 2.Institute of Computer ScienceSilesian University of TechnologyPoland
  3. 3.Institute of Medical Technology and EquipmentPoland

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