SpringerLink
Log in

The Quantification of the QT-RR Interaction in ECG Signal Using the Detrended FluctuationAnalysis and ARARX Modelling

  • Education & Training
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this paper, the detrended fluctuation analysis DFA is used to investigate and quantify the QT-RR interaction in different pathologic cases in order to distinguish between them. The study is carried out on the ECG signals of MIT-BIH universal database. Different ECG signals related to cardiac pathological cases are concerned with this study. These are: Premature Ventricular Contraction (PVC) (9 cases), Right Bundle Branch Block (RBBB) (4 cases), Left Bundle Branch Block (LBBB) (2 cases), Atrial Premature Beat (APB) (4 cases), Paced Beat (PB) (4 cases), and other pathologic cases with different severity (10 cases). All this cases are compared to the 15 normal cases. The obtained results show that the DFA can identify the healthy subject from the pathologic cases according to the values of the scaling exponent α. The results indicate that α varies between 0.5 and 1 in all cases which means that there is a long range correlation in RR and QT series. The QT and RR series are also modelled using the ARARX model. The parameters of the model are then extracted. The power spectral density (PSD) is estimated by using these parameters in order to provide further information about the causal interactions within the signals and also to determine the power scaling exponent β. This scaling exponent confirms the relationship between RR and QT intervals in all the studied cases except in APB and PB cases where the behaviour is similar to that of the white noise. The QT variability degrees are calculated and the DFA is applied on it. The obtained results show a long range correlation between RR and QT intervals in all cases and an ambiguity in the APB case. The DFA is compared to the Poincaré method in order to evaluate the algorithm performance using the Fuzzy Sugeno classifier is used for this purpose.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Robert, M. C., Freedland, K. E., Stein, P. K., Watkins, L. L., Catellier, D., Jaffe, A. S., and Yeragani, V. K., Effects of depression on QT interval variability after myocardial infarction. Psychosomatic Medicine 65:177–180, 2003.

    Article  Google Scholar 

  2. Piccirillo, G., Cacciafesta, M., and Lionetti, M., Influence of age, the autonomic nervous system and anxiety on QT-interval variability. Clin Sci (Lond) 101(4):429–438, 2001.

    Article  CAS  Google Scholar 

  3. Almeida, R., Gouveia, S., Rocha, A. P., Pueyo, E., Martinez, J. P., and Laguna, P., QT variability and HRV interactions in ECG : Quantification and reliability. IEEE Transactions On Biomedical Engineering 53:1317–1329, 2006.

    Article  PubMed  Google Scholar 

  4. Peng, C.-K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., and Goldberger, A. L., Mosaic organization of DFA nucleotides. Physical Review E 49:1685–1689, 1994.

    Article  CAS  Google Scholar 

  5. Weron, R., Estimating long-range dependence: Finite sample properties and confidence intervals. Physica A 312(1–2):285–299, 2002.

    Article  Google Scholar 

  6. Stam, C. J., Montez, T., Jones, B. F., Rombouts, S., van der Made, Y., Pijnenburg, Y. A. L., and Scheltens, P., Disturbed fluctuations of resting state EEG synchronization in Alzheimer’s disease. Clinical Neurophysiology 116:708–715, 2005.

    Article  CAS  PubMed  Google Scholar 

  7. Yeh, R.-G., Shing, J.-S., Y-Y Han, Y.-J., and Tseng, S.-C., Detrended fluctuation analysis in short-term heart rate variability in surgical intensive care units. Biomed Eng Appl Basis Comm 18:67–72, 2006.

    Article  Google Scholar 

  8. Penzel, T., Kantelhardt, J. W., Grote, L., Peter, J.-H., and Bunde, A., Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Transactions On Biomedical Engineering 50:1143–1151, 2003.

    Article  PubMed  Google Scholar 

  9. Ivanov, P. C., Scale invariant aspects of cardiac dynamics observing sleep stages and circadian phases. IEEE Engineering In Medicine and Biology Magazine 26:33–37, 2007.

    Article  PubMed  Google Scholar 

  10. Yeh, J.-R., Fan, S.-Z., and Shieh, J.-S., Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition. Medical Engineering & Physics 31:92–100, 2009.

    Article  Google Scholar 

  11. Iyengar, N., Peng, C. K., Morin, R., Goldberger, A. L., and Lipsitz, L. A., Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol. 271:R1078–R1084, 1996.

    CAS  PubMed  Google Scholar 

  12. Plamen Ch, I. V. A. N. O. V., Scale-invariant aspects of cardiac dynamics across sleep stages and circadian phases. IEEE Engineering in medicine an biology magazine 26:33–37, 2007.

    Article  Google Scholar 

  13. Peng, C. K., Havlin, S., Stanley, H. E., and Goldberger, A. L., Quantification of scaling exponents and crossover phenomena institute of physics 5:82–87, 1995.

    CAS  Google Scholar 

  14. Peng, C. K., Mietus, J., Hausdorff, J. M., Havlin, S., Stanley, H. E., and Goldberger, A. L., long range anticorrelation and non-Gaussian behavior of the heartbeat”. Physical review letters 70:1343–1346, 1993.

    Article  Google Scholar 

  15. Mathias Baumert, Michal Javorkac, Andrea Seeck, Renaldo Faber, Prashanthan Sanders, Andreas Voss, “Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy”, Computers in Biology and Medicine, 2011, doi:10.1016/j.compbiomed.2011.03.019

  16. Hadj Slimane, Z. E., and Bereksi Reguig, F., Detection of the QRS complex by linear prediction. Journal of Medical Engineering & Technology 30:134–138, 2006.

    Article  CAS  Google Scholar 

  17. Laguna, P., Thakor, N. V., Caminal, P., Jané, R., Yoon, H.-R., de Luna, A. B., Marti, V., and Guindo, J., New algorithm for QT interval analysis in 24 h holter ECG: performance and applications. Med & Biol Eng & Comput 28:67–73, 1990.

    Article  CAS  Google Scholar 

  18. Fernanda S. Leite, Adson F. da Rocha, and Jo˜ao L. A. Carvalho, Matlab software for detrended fluctuation analysis of heart rate variability, International Conference on Bio-inspired Systems and Signal Processing, 225–229, Biosignals (2010)

  19. Porta, A., Baselli, G., Caiani, E., Malliani, A., Lombardi, F., and Cerutti, S., Quantifying electrocardiogram RT-RR variability interactions. Medical & Biological Engineering & Computing 36:27–34, 1998.

    Article  CAS  Google Scholar 

  20. Baakek Y N, évaluation et quantification de l’intervalle RR et QT dans différents cas pathologiques, Magister thesis, University of Tlemcen, Algeria. February, (2009)

  21. Baselli, G., Porta, A., Rimoldi, O., Pagani, M., and Cerutti, S., Spectral decomposition in multichannel recordings based on multivariate parametric identification, IEEE trans. BME 44:1092–1101, 1997.

    Article  CAS  Google Scholar 

  22. Piskorski, J., and Guzik, P., Geometry of the Poincar´e plot of RR intervals and its asymmetry in healthy adults. Physiol. Meas 28:287–300, 2007.

    Article  CAS  PubMed  Google Scholar 

  23. Liu, J., Detrended fluctuation analysis of vibration signals for bearing fault detection, prognostics and health management (PHM). IEEE(1–5), 2011.

  24. Montri Phothisonothai and Katsumi Watanabe . Optimal Fractal Feature and Neural Network: EEG Based BCI Applications, Brain-Computer Interface Systems - Recent Progress and Future Prospects, Dr. Reza Fazel-Rezai (Ed.), ISBN: 978-953-51-1134-4, InTech, DOI: 10.5772/55801, 2013

  25. Bari, V., Bassani, T., Marchi, A., Girardengo, G., Calvillo, L., Cerutti, S., Brink, P. A., Crotti, L., Schwartz, P. J., and Porta, A., Symbolic analysis of heart period and QT interval variabilities in LQT1 patients, XIII Mediterranean conference on medical and biological engineering and computing 2013. IFMBE Proceedings Volume 41:531–534, 2014.

    Article  Google Scholar 

  26. Peng, Y., and Sun, Z., Characterization of QT and RR interval series during acute myocardial ischemia by means of recurrence quantification analysis. Medical & Biological Engineering & Computing 49(1):25–31, 2011.

    Article  Google Scholar 

  27. Übeyli, E. D., Cvetkovic, D., and Cosic, I., AR spectral analysis technique for human PPG, ECG and EEG signals. Journal of Medical Systems 32(3):201–206, 2008.

    Article  PubMed  Google Scholar 

  28. Abibullaev, B., and Seo, H. D., A new QRS detection method using wavelets and artificial neural networks. Journal of Medical Systems 35(4):683–691, 2011.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biomedical Engineering Laboratory (GBM) Electrical Engineering & Electronics Department, Faculty of Technology, Tlemcen University, B.P.119, Tlemcen, 13000, Algeria

    Y. N. Baakek & Z. E. Hadj Slimane

  2. Faculty of Technology, Tlemcen University, B.P.119, Tlemcen, 13000, Algeria

    F. Bereksi Reguig

Authors
  1. Y. N. Baakek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Z. E. Hadj Slimane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Bereksi Reguig
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. N. Baakek.

Additional information

This article is part of the Topical Collection on Education & Training

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baakek, Y.N., Hadj Slimane, Z.E. & Bereksi Reguig, F. The Quantification of the QT-RR Interaction in ECG Signal Using the Detrended FluctuationAnalysis and ARARX Modelling. J Med Syst 38, 62 (2014). https://doi.org/10.1007/s10916-014-0062-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-014-0062-9

Keywords

Search

Navigation