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A comparative study between normal electrocardiogram signal and those of some cardiac arrhythmias based on McSharry mathematical model

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Abstract

In this paper, synthetic electrocardiogram signals (SECG) of eight cardiac arrhythmias (sinus bradycardia, junctional bradycardia, tachycardia, flutter, atrial extrasystole, ventricular extrasystole, left branch block and right branch block) are obtained numerically by solving the McSharry mathematical model (2003) based on three coupled ordinary differential equations with the fourth-order Runge–Kutta method. They are compared with normal electrocardiogram signal. Indeed, visual analysis of a section of electrocardiogram (ECG) signals of these arrhythmias was used to suggest suitable values for the parameters in the McSharry mathematical model. Results from numerical simulation showed a good agreement between the simulation results and the real cardiac arrhythmias ECG signals.

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References

  1. Gutierrez A, Lara M, Hernandez PR (2005) A QRS detector based on Haar wavelet, evaluation with MIT-BIH arrhythmia and European ST-T Databases. Computacion y Sistemas. 8:293–302

    Google Scholar 

  2. Kaneko M, Gotho T, Iseri F, Takeshita K, Ohki H, Sueda N (2011) QRS complex analysis using wavelet transform and two layered self-organizing map. In: Computing in cardiology. IEEE, New York, pp 813–816

  3. Addison PS (2005) Wavelet transforms and the ECG: a review. Physiol Meas 26(5):R155–R199

    Article  PubMed  Google Scholar 

  4. Burke MJ, Nasor M (2002) The time relationships of the constituent components of the human electrocardiogram. J Med Eng Technol 26(1):1–6

    Article  CAS  PubMed  Google Scholar 

  5. Schuck A, Wisbeck JO (2003) QRS detector pre-processing using the complex wavelet transform, vol 3. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society, 2003. IEEE, New York, pp 2590–2593

  6. Vassilikos VP, Mantziari L, Dakos G, Kamperidis V, Chouvarda I, Chatzizisis YS et al (2014) QRS analysis using wavelet transformation for the prediction of response to cardiac resynchronization therapy: a prospective pilot study. J Electrocardiol 47(1):59–65

    Article  PubMed  Google Scholar 

  7. Ieong CI, Mak PI, Lam CP, Dong C, Vai MI, Mak PU et al (2012) 0.83-QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35-CMOS. IEEE Trans Biomed Circuits Syst 6(6):586–595

    Article  PubMed  Google Scholar 

  8. Zeng C, Lin H, Jiang Q, Xu M (2013) QRS complex detection using combination of mexican-hat wavelet and complex morlet wavelet. JCP 8(11):2951–2958

    Google Scholar 

  9. Kadambe S, Murray R, Boudreaux-Bartels GF (1999) Wavelet transform-based QRS complex detector. IEEE Trans Biomed Eng 46(7):838–848

    Article  CAS  PubMed  Google Scholar 

  10. Hamilton PS, Tompkins WJ (1986) Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans Biomed Eng 12:1157–1165

    Article  Google Scholar 

  11. Okada M (1979) A digital filter for the ors complex detection. IEEE Trans Biomed Eng 12:700–703

    Article  Google Scholar 

  12. Jaswal G, Parmar R, Kaul A (2012) QRS detection using wavelet transform. Int J Eng Adv Technol 1(6):1–5

    Google Scholar 

  13. Dinh HAN, Kumar DK, Pah ND, Burton P (2001) Wavelets for QRS detection. Aust Phys Eng Sci Med 24(4):207

    Article  CAS  Google Scholar 

  14. Alvarado C, Arregui J, Ramos J, Pallàs-Areny R (2005) Automatic detection of ECG ventricular activity waves using continuous spline wavelet transform. In: 2005 2nd international conference on electrical and electronics engineering. IEEE, New York, pp. 189–192

  15. Manikandan MS, Soman KP (2012) A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed Signal Process Control 7(2):118–128

    Article  Google Scholar 

  16. Gutiérrez-Gnecchi JA, Morfin-Magana R, Lorias-Espinoza D, del Carmen Tellez-Anguiano A, Reyes-Archundia E, Méndez-Patiño A, Castañeda-Miranda R (2017) DSP-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomed Signal Process Control 32:44–56

    Article  Google Scholar 

  17. Holden AV, Poole MJ, Tucker JV (1995) Reconstructing the heart. Chaos Solitons Fractals 5(3–4):691–704

    Article  Google Scholar 

  18. Boyett M, Holden AV, Kodama I, Suzuki R, Zhang H (1995) Atrial modulation of sinoatrial pacemaker rate. Chaos Solitons Fractals 5(3–4):425–438

    Article  CAS  Google Scholar 

  19. Holden AV, Biktashev VN (2002) Computational biology of propagation in excitable media models of cardiac tissue. Chaos Solitons Fractals 13(8):1643–1658

    Article  Google Scholar 

  20. Poole MJ, Holden AV, Tucker JV (2002) Hierarchical reconstructions of cardiac tissue. Chaos Solitons Fractals 13(8):1581–1612

    Article  Google Scholar 

  21. Gois SR, Savi MA (2009) An analysis of heart rhythm dynamics using a three-coupled oscillator model. Chaos Solitons Fractals 41(5):2553–2565

    Article  Google Scholar 

  22. Tlili M, Maalej A, Romdhane MB, Rivet F, Dallet D, Rebai C (2016) Mathematical modeling of clean and noisy ECG signals in a level-crossing sampling context. In: International symposium on signal, image, video and communications (ISIVC). IEEE, New York, pp 359–363

  23. Wu HT, Wu HK, Wang CL, Yang YL, Wu WH et al (2016) Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform. PLoS ONE 11(6):e0157135. https://doi.org/10.1371/journal.pone.0157135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tripathy RK, Mendez AZ, de la Serna JAO, Arrieta Paternina MR, Arrieta JG (2018) Naik GR (2018) Detection of life threatening ventricular arrhythmia using digital taylor fourier transform. Front Physiol 9:722

    Article  PubMed  PubMed Central  Google Scholar 

  25. Raj S, Ray KC (2018) Sparse representation of ECG signals for automated recognition of cardiac arrhythmias. Expert Syst Appl 105:49–64

    Article  Google Scholar 

  26. Raka AG, Naik GR, Chai R (2017) Computational algorithms underlying the time-based detection of sudden cardiac arrest via electrocardiographic markers. Appl Sci 7(9):954

    Article  Google Scholar 

  27. de Albuquerque VHC, Nunes TM, Pereira DR, Luz EJDS, Menotti D, Papa JP, Tavares JMR (2018) Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput Appl 29(3):679–693

    Article  Google Scholar 

  28. http://samples.jbpub.com/9781449652609/99069_ch05_6101.pdf. Accessed 05 Dec 2018

  29. McSharry PE, Clifford GD, Tarassenko L, Smith LA (2003) A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng 50(3):289–294

    Article  PubMed  Google Scholar 

  30. https://www.physionet.org. Accessed 08 Nov 2018

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Acknowledgements

The authors are grateful to Dr. YIAGNIGNI Euloge, cardiologist at the health center “Les promoteurs de la bonne santé” for his fruitful advices.

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No funding was received.

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Authors and Affiliations

  1. Université des Montagnes, ISST, P.O. Box 208, Bangangté, Cameroon

    Pascalin Tiam Kapen

  2. University of Dschang, LISIE/L2MSP, P.O. Box 134, Bandjoun, Cameroon

    Pascalin Tiam Kapen, Serge Urbain Kouam Kouam & Ghislain Tchuen

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  1. Pascalin Tiam Kapen
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  2. Serge Urbain Kouam Kouam
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  3. Ghislain Tchuen
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Correspondence to Pascalin Tiam Kapen.

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Pascalin TIAM KAPEN declares that he has no conflict of interest. KOUAM KOUAM Serge Urbain declares that he has no conflict of interest. TCHUEN Ghislain declares that he has no conflict of interest.

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Tiam Kapen, P., Kouam Kouam, S. & Tchuen, G. A comparative study between normal electrocardiogram signal and those of some cardiac arrhythmias based on McSharry mathematical model. Australas Phys Eng Sci Med 42, 511–528 (2019). https://doi.org/10.1007/s13246-019-00752-7

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  • DOI: https://doi.org/10.1007/s13246-019-00752-7

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