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Some False ECG Waves Detections Revised by Fractal Dimensions

  • Ibticeme Sedjelmaci
  • Fethi Bereksi Reguig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)

Abstract

In this paper, we used the fractal dimensions in ECG signals to identify the wave’s detections failure. We check for the sensitivities and the importance of QRS and ST detection because different false wave’s detections caused by the various types of interference and artefact are detected for some ECG signals presenting pathologies.

The fractal dimension is very sensitive to variations: if irregularities degree is great, the fractal dimension is high and vice versa. Different cases of pathologies decreased irregularities on the ECG signal so it causes a decrease in fractal dimension. However decreasing in irregularities is not necessarily pathological: a bad detection can also train it, because we have not the exact location of the beginning and the end of QRS complex or the end of the T wave, it causes a new variation in the dimension fractal which can skew the result.

For that reason and in order to get good results from algorithm detection, the fractal dimensions are calculated for each QRS complex and ST segment, for some ECG signals, to check their sensitivities in heart rate irregularities and false wave’s detections so that make ECG interpretation system more effective.

Keywords

Electrocardiogram signals (ECG) QRS and ST detection algorithm Fractal dimension 

References

  1. 1.
    Véhel, J.L.: Analyse Fractale: une nouvelle génération d’outils pour le Traitement du Signal. INRIA - Projet Fractales (2000)Google Scholar
  2. 2.
    Barrière, O., Véhel, J.L.: Local Hölder regularity-based modeling of RR intervals. Project APIS INRIA Saclay (2008)Google Scholar
  3. 3.
    Smrčka, P., Bittner, R., Vysoký, P., Hána, K.: Fractal and multifractal properties of heartbeat interval series in extremal states of the human organism. Measur. Sci. Rev. 3, 13–15 (2003)Google Scholar
  4. 4.
    Lopes, R., Dubois, P., Bhouri, I., Akkari-Bettaieb, H., Maouche, S., Betrouni, N.: La géométrie fractale pour l’analyse de signaux médicaux: état de l’art. IRBM 31, 189–208 (2010)CrossRefGoogle Scholar
  5. 5.
    Islam, N., Hamid, N.I.B., Mahmud, A., Rahman, S.M., Khan, A.H.: Detection of some major heart diseases using fractal analysis. Int. J. Biom. Bioinfo. (IJBB) 4(2), 63 (2010)Google Scholar
  6. 6.
    Lee, J.S., Chang, K.S.: Applications of chaos and fractals in process systems engineering. J. Process Control 6, 71–87 (1996)CrossRefGoogle Scholar
  7. 7.
    Zhang, X.S., Zhu, Y.S., Zhang, X.J.: New approach to studies on ECG dynamics: extraction and analyses of QRS complex irregularity time series. Med. Biol. Eng. Comput 35, 467–474 (1997)CrossRefGoogle Scholar
  8. 8.
    Theiler, J.: Estimating fractal dimension. J. Opt. Soc. Am. A 7(6), 1055–1073 (1990)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Jaffard, S.: Sur la dimension de boite des graphes. Math. Anal. 326, 555–560 (1998)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Roueff, F., Véhel, J.L.: A regularization approach to fractional dimension estimation. In: Proceeding of Fractals 1998, Malta, October 1998Google Scholar
  11. 11.
    Pan, J., Tompkins, J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32, 230–236 (1985)CrossRefGoogle Scholar
  12. 12.
    Chan, K., So, H.: Development of QRS method for real-time ambulatory cardiac monitor. In: Proceedings of 19 Annual of International Conference IEEE EMBS, Chicago, USA, pp. 289–292 (1997)Google Scholar
  13. 13.
    Zhang, Q., Manriquez, A.I., Médigue, C., Papelier, Y., Sorine, M.: An algorithm for robust and efficient location of T-wave ends in electrocardiogram. Irisa Publication Interne1744 (2005). www.irisa.fr
  14. 14.
    Clifford, G.D.: ECG statistics, noise, artifacts, and missing data. In: Advanced Methods & Tools for ECG Data Analysis, chap. 3Google Scholar
  15. 15.
    Sedjelmaci, I., Bereksi Reguig, F.: La Théorie du Chaos et l’Analyse Fractale dans la Variabilité du Rythme Cardiaque. In: Biomedical Engineering International Conference, BIOMEIC 2012, Tlemcen (2012)Google Scholar
  16. 16.
    Sedjelmaci, I., Bereksi Reguig, F.: Detection of some heart diseases using fractal dimension and chaos theory. In: The 8th International Workshop on Systems, Signal Processing and their Applications, WOSSPA 2013, Alger (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical Systems Engineering DepartmentUMBB UniversityBoumerdèsAlgeria
  2. 2.Biomedical Engineering LaboratoryABBT UniversityTlemcenAlgeria

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