Time-Frequency Analysis of Electric Cardiograms

Abstract

In the present work, statistical processing of the electric cardiogram (spectral and bispectral analysis) using the ‘sliding window’ method is proposed and performed. A system for recording and digitizing an electric cardiograms was developed, the output signal of which is fed to a computer. Signal processing is carried out using a system implemented in the LabVIEW environment. It is shown that the time-frequency analysis using the ‘sliding window’ method allows detecting dynamic processes in the work of the human heart, which can go unnoticed in standard analyzes. Research results can be useful for the diagnosis of heart disease.

This is a preview of subscription content, access via your institution.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

REFERENCES

  1. 1

    McCraty, R., Exploring the Role of the Heart in Human Performance, Vol. 2, HeartMath Research Center, 2015, p. 118.

    Google Scholar 

  2. 2

    Kuznetsov, A.A., Biofizika Serdtsa (Biophysics of the Heart), Vladimir: VlGU, 2012, p. 237.

  3. 3

    Waller, A.D., The Journal of physiology, 1887, vol. 8, no. 5, p. 229.

    Article  Google Scholar 

  4. 4

    Burch, G.E. and DePasquale, N.P., A history of electrocardiography, Norman Publishing, 1990.

    Google Scholar 

  5. 5

    Berkaya. S.K. et al., Biomedical Signal Processing and Control, 2018, vol. 43, p. 216.

    Article  Google Scholar 

  6. 6

    Sornmo, L. and Laguna, P., Electrocardiogram (ECG) Signal Processing, Wiley Encyclopedia of Biomedical Engineering, 2006.

    Google Scholar 

  7. 7

    Chua, C.K., Chandran, V., Acharya, R., and Min, L.C., Medical Engineering and Physics, 2010, vol. 32, no. 7, p. 679.

    Article  Google Scholar 

  8. 8

    Wang, J., Wang, P., and Wang, S., Biomedical Signal Processing and Control, 2020, vol. 55, p. 1.

    Article  Google Scholar 

  9. 9

    Ghista, D.N., et al., Journal of Medical Systems, 2010, vol. 34, p. 445.

    Article  Google Scholar 

  10. 10

    Moeyersons, J., Smets, E., Morales, J., et al., Computer Methods and Programs in Biomedicine, 2019, vol. 182, p. 105050.

    Article  Google Scholar 

  11. 11

    Istepanian, R.S.H., Hadjileontiadis, L.J., and Panas, S.M., IEEE Transactions on Information Technology in Biomedicine, 2001, vol. 5, no. 2, p. 108.

    Article  Google Scholar 

  12. 12

    Zelensky, A.A., Kravchenko, V.F., Pavlikov, V.V., and Totsky, A.V., Fizicheskiye osnovy priborostroyeniya (Physical foundations of instrumentation), 2013, vol. 2, no. 3, p. 4 [in Russian].

  13. 13

    Petropulu, A.P., Higher-Order Spectral Analysis, CRC Press LLC, 2000.

    Google Scholar 

  14. 14

    Chang, J.H. and Lee, W.S., Journal of Information Science, 2005, vol. 31, p. 76.

    Article  Google Scholar 

  15. 15

    Totsky, A.V., Systemy obrobky ynformatsyy (Information Processing Systems), 2009, vol. 3, p. 108 [in Ukrainen].

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to A. O. Makaryan.

Ethics declarations

The authors declare no conflict of interest.

Additional information

Translated by V.M. Aroutiounian

About this article

Verify currency and authenticity via CrossMark

Cite this article

Oganisyan, B.A., Oganesyan, T.N. & Makaryan, A.O. Time-Frequency Analysis of Electric Cardiograms. J. Contemp. Phys. 55, 371–375 (2020). https://doi.org/10.3103/S1068337220040155

Download citation

Keywords:

  • ECG
  • statistical signal processing
  • spectrum
  • bispectral analysis