Time-Frequency Analysis Based Detection of Dysrhythmia in ECG Using Stockwell Transform

  • Yengkhom Omesh Singh
  • Sushree Satvatee SwainEmail author
  • Dipti PatraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)


Dysrhythmia is the abnormality in rhythm of our cardiac activity. Dysrhythmia is mainly caused by the re-entry of the electric impulse resulting abnormal depolarization of the myocardium cells. Sometimes, such activity causes life threatening ailments. Several myocardial diseases have been studied and detected by help of time frequency representation based techniques effectively. So, a powerful tool called Stockwell transform (ST) has been evolved to provide better time-frequency localization. Wavelet transform based methods arose as an challenging tool for the analysis of ECG signals with both the temporal and the frequency resolution levels. In this study, S-Transform based time-frequency analysis is adopted to detect the Dysrhythmia in ECG signal at high frequencies, which is difficult to study by using Continuous Wavelet transform (CWT). The time frequency analysis is performed over 3 frequency ranges namely low frequency (1–15 Hz) zone, mid-frequency (15–80 Hz) zone and high-frequency (>80 Hz) zone and their respective Integrated time-frequency Power (ITFP) are calculated. The patients with Dysrhythmia has higher ITFP in the high frequency zone than the healthy individuals. The accuracy of the detection of Dysrhythmia is found out to be 88.09% using ST whereas CWT method provides only 58.62% detection accuracy.


Dysrhythmia Stockwell transform (ST) Continuous Wavelet transform (CWT) Integrated time-frequency Power (ITFP) 


  1. 1.
    German-Sallo, Z.: ECG signal baseline wander removal using wavelet analysis. In: Vlad, S., Ciupa, R.V. (eds.) International Conference on Advancements of Medicine and Health Care through Technology. IFMBE, vol. 36, pp. 190–193. Springer, Heidelberg (2011). Scholar
  2. 2.
    Gramatikov, B., Brinker, J., Yi-Chun, S., Thakor, N.V.: Wavelet analysis and time-frequency distributions of the body surface ECG before and after angioplasty. Comput. Methods Programs Biomed. 62(2), 87–98 (2000)CrossRefGoogle Scholar
  3. 3.
    Gramatikov, B., Georgiev, I.: Wavelets as alternative to short-time Fourier transform in signal-averaged electrocardiography. Med. Biol. Eng. Comput. 33(3), 482–487 (1995)CrossRefGoogle Scholar
  4. 4.
    Lu, W.K., Zhang, Q.: Deconvolutive short-time Fourier transform spectrogram. IEEE Sig. Process. Lett. 16(7), 576–579 (2009)CrossRefGoogle Scholar
  5. 5.
    Morlet, D., Peyrin, F., Desseigne, P., Touboul, P., Rubel, P.: Wavelet analysis of high-resolution signal-averaged ECGs in postinfarction patients. J. Electrocardiol. 26(4), 311–320 (1993)CrossRefGoogle Scholar
  6. 6.
    Reynolds Jr., E.W., Muller, B.F., Anderson, G.J., Muller, B.T.: High-frequency components in the electrocardiogram: a comparative study of normals and patients with myocardial disease. Circulation 35(1), 195–206 (1967)CrossRefGoogle Scholar
  7. 7.
    Sadhukhan, D., Mitra, M.: Detection of ECG characteristic features using slope thresholding and relative magnitude comparison. In: 2012 Third International Conference on Emerging Applications of Information Technology, pp. 122–126. IEEE (2012)Google Scholar
  8. 8.
    Sadhukhan, D., Pal, S., Mitra, M.: Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data. IEEE Trans. Instrum. Meas. 67(10), 2303–2313 (2018)CrossRefGoogle Scholar
  9. 9.
    Stockwell, R., Mansinha, L., Lowe, R.: Localisation of the complex spectrum: the S transform. J. Assoc. Explor. Geophys. 17(3), 99–114 (1996)Google Scholar
  10. 10.
    Stockwell, R.G.: A basis for efficient representation of the S-transform. Digit. Sig. Process. 17(1), 371–393 (2007)CrossRefGoogle Scholar
  11. 11.
    Takano, N.K., Tsutsumi, T., Suzuki, H., Okamoto, Y., Nakajima, T.: Time frequency power profile of QRS complex obtained with wavelet transform in spontaneously hypertensive rats. Comput. Biol. Med. 42(2), 205–212 (2012)CrossRefGoogle Scholar
  12. 12.
    Thomas, L.J., Clark, K.W., Mead, C.N., Ripley, K., Spenner, B., Oliver, G.: Automated cardiac dysrhythmia analysis. Proc. IEEE 67(9), 1322–1337 (1979)CrossRefGoogle Scholar
  13. 13.
    Tripathy, R.K., Paternina, M.R., Arrieta, J.G., Zamora-Méndez, A., Naik, G.R.: Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. Comput. Methods Programs Biomed. 173, 53–65 (2019)CrossRefGoogle Scholar
  14. 14.
    Tsutsumi, T., et al.: Time-frequency analysis of the QRS complex in patients with ischemic cardiomyopathy and myocardial infarction. IJC Hear. Vessel. 4, 177–187 (2014)CrossRefGoogle Scholar
  15. 15.
    Wang, Y.H., et al.: The tutorial: S transform. Graduate Institute of Communication Engineering, National Taiwan University, Taipei (2010)Google Scholar
  16. 16.
    Xia, Y., et al.: An automatic cardiac arrhythmia classification system with wearable electrocardiogram. IEEE Access 6, 16529–16538 (2018)CrossRefGoogle Scholar
  17. 17.
    Zhu, H., et al.: A new local multiscale Fourier analysis for medical imaging. Med. Phys. 30(6), 1134–1141 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IPCV Lab, Department of Electrical EngineeringNIT RourkelaRourkelaIndia
  2. 2.Department of Electrical EngineeringNIT RourkelaRourkelaIndia

Personalised recommendations