Instantaneous Measurement of SNR in Electrocardiograms Based on Quasi-continuous Time-Scale Noise Modeling

  • Piotr Augustyniak
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


Proper measurement of signal quality is a common problem in biomedical applications, including the electrocardiogram interpretation. The need for a reliable signal-to-noise estimate raises with the common use of telemedical recordings performed in the home care conditions and interpreted automatically. The currently used techniques perform noise measurements on the baseline and extrapolate the results to a whole heart beat. Main drawback of this method lies in irregular occurrence and short duration of the baseline. This paper presents a new ECG-dedicated noise estimation technique based on a time-frequency model of noise computed in a quasi-continuous way. The proposed algorithm uses the temporarily adapted local bandwidth variability of cardiac electrical representation to recognize cardiac components. The part of the time-frequency plane remaining above the local bandwidth of the signal, represents background activities of any origin (muscle, mains interference etc.). This noise estimate in each particular scale is available as non-uniformly sampled and has to be interpolated to the regions where components of cardiac representation are expected. In lower scales, the noise contribution is computed with use of square polynomial extrapolation. The algorithm was implemented and tested with use of the CSE Database records with the addition of the MIT-BIH Database noise patterns. The differences between the added and estimated noise show similar performance of baseline-based and noise model-based methods (0.69 dB and 0.64 dB respectively) as long as the noise level is stable. However when dynamic noise occurs, the baseline-based method is outperformed by the proposed algorithm (2.90 dB and 0.95 dB respectively) thanks to consideration of multiple measurement points and accurate noise tracking.


Nyquist Frequency Noise Contribution Dynamic Noise Biomedical Signal Processing Local Bandwidth 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Akay, M.: Biomedical signal processing. Academic Press, San Diego (1994)Google Scholar
  2. 2.
    Akay, M. (ed.): Wavelets in Biomedical Signal Processing. IEEE Press, New York (1998)zbMATHGoogle Scholar
  3. 3.
    Aldroubi, A., Feichtinger, H.: Exact iterative reconstruction algorithm for multivariate irregularly sampled functions in spline-like spaces: the Lp theory. Proc. Amer. Math. Soc. 126(9), 2677–2686 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Augustyniak, P.: Controlling the Distortions Distribution in a Wavelet Packet-Based ECG Compression. In: International Conference on Image and Signal Processing, Agadir Morroco, pp. 267–277 (2001)Google Scholar
  5. 5.
    Augustyniak, P.: How a Human Perceives the Electrocardiogram. Computers in Cardiology 30, 601–604 (2003)Google Scholar
  6. 6.
    Augustyniak, P.: Time-frequency modelling and discrimination of noise in the electrocardiogram. Physiol. Meas. 24(3), 753–767 (2003)CrossRefGoogle Scholar
  7. 7.
    Augustyniak, P.: Separating Cardiac and Muscular ECG Components Using Adaptive Modelling in Time-Frequency Domain. In: Proceedings of the WACBE World Congress on Bioengineering, paper 184 (2007)Google Scholar
  8. 8.
    Augustyniak, P.: Moving Window Signal Concatenation for Spectral Analysis of ECG Waves. Computing in Cardiology 37, 665–668 (2010)Google Scholar
  9. 9.
    Calderbank, A.R., Daubechies, I., Sweldens, W., Yeo, B.L.: Wavelet transforms that map integers to integers. Technical report, Department of Mathematics, Princeton University (1996)Google Scholar
  10. 10.
    Krishnan, S., Rangayyan, R.M.: Automatic de-noising of knee-joint vibration signals using adaptive time-frequency representations. Med. Biol. Eng. Comput. 38, 2–8 (2000)CrossRefGoogle Scholar
  11. 11.
    Moody, G.B.: The MIT-BIH arrhythmia database CD-ROM, 3rd edn. Harvard-MIT Division of Health Sciences and Technology (1997)Google Scholar
  12. 12.
    Moss, A., Stern, S.: Noninvasive Electrocardiology - clinical aspects of Holter monitoring. Saunders Co., London (1996)Google Scholar
  13. 13.
    Nikolaev, N., Gotchev, A.: De-noising of ECG signals using wavelet shrinkage with time-frequency dependant threshold. In: Proc. European Signal Processing Conf., EUSIPCO 1998, Island of Rhodes, Greece, pp. 2449–2453 (1998)Google Scholar
  14. 14.
    Nikolaev, N., Gotchev, A., Egiazarian, K., Nikolov, Z.: Suppression of electromyogram interference on the electrocardiogram by transform domain denoising. Med. Biol. Eng. Comput. 39, 649–655 (2001)CrossRefGoogle Scholar
  15. 15.
    Paul, J., Reedy, M., Kumar, V.: A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG’s. IEEE Trans. Biomed. Eng. 47, 645–662 (2000)CrossRefGoogle Scholar
  16. 16.
    Willems, J.L.: Common Standard for Quantitative Electrocardiography Multilead Atlas - Measurements results Data Set 3. Commission of the European Communities - Medical and Public Health Research, Leuven (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Piotr Augustyniak
    • 1
  1. 1.AGH University of Science and TechnologyKrakówPoland

Personalised recommendations