International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.5, No.2, Feb. 2013

Successive RR Interval Analysis of PVC With Sinus Rhythm Using Fractal Dimension, Poincaré Plot and Sample Entropy Method

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Md. Meganur Rhaman, A. H. M. Zadidul Karim, Md. Maksudul Hasan, Jarin Sultana

Index Terms

ECG, Fractal Dimension, PVC, Sample Entropy, Poincaré plot


Premature ventricular contractions (PVC) are premature heartbeats originating from the ventricles of the heart. These heartbeats occur before the regular heartbeat. The Fractal analysis is most mathematical models produce intractable solutions. Some studies tried to apply the fractal dimension (FD) to calculate of cardiac abnormality. Based on FD change, we can identify different abnormalities present in Electrocardiogram (ECG). Present of the uses of Poincaré plot indexes and the sample entropy (SE) analyses of heart rate variability (HRV) from short term ECG recordings as a screening tool for PVC. Poincaré plot indexes and the SE measure used for analyzing variability and complexity of HRV. A clear reduction of standard deviation (SD) projections in Poincaré plot pattern observed a significant difference of SD between healthy Person and PVC subjects. Finally, a comparison shows for FD, SE and Poincaré plot parameters.

Cite This Paper

Md. Meganur Rhaman, A. H. M. Zadidul Karim, Md. Maksudul Hasan, Jarin Sultana,"Successive RR Interval Analysis of PVC With Sinus Rhythm Using Fractal Dimension, Poincaré Plot and Sample Entropy Method", IJIGSP, vol.5, no.2, pp.17-24, 2013.DOI: 10.5815/ijigsp.2013.02.03


[1]J. E. Keany and A. D. Desai, "Premature ventricular contraction" in article/761148-overview.

[2]D. A. Litvack, T. F. Oberlander, L. H. Carney and J. P. Saul, "Time and frequency domain methods for heart rate variability analysis: a methodological comparison," Psychophysics, vol. 32, pp. 492–504, 1995.

[3]N. V. Thakor and K. Pan, "Tachycardia and fibrillation detection by automatic implantable cardioverter-defibrillators: sequential testing in time domain," IEEE Trans. Biomed. Eng., vol. 9, pp. 21-24, 1990.

[4]P. Guzik, J. Piskorski, T. Krauze, R. Schneider, K. H. Wesseling, A. W. Towicz and H. Wysocki, "Correlations between the Poincaré Plot and conventional heart rate variability parameters assessed during paced breathing," J. Physiol. Sci., vol. 57, pp. 63–71, 2007.

[5]J. S. Richman and J. R. Moorman, "Physiological time-series analysis using approximate entropy and sample entropy," Am J Physiol, vol. 278, pp. 2039–2049, 2000.

[6]K. Falconer, Fractal Geometry: Mathematical Foundations and Applications, John Wiley & Sons, 1990, vol. 1, pp. 110-112.

[7]W. Deering and B. J. West, "Fractal Physiology," IEEE EMB Mag., vol. 12, pp. 40-46, 1992.

[8]A. L. Goldberger, D. R. Rigney and B. J. West, "Chaos and fractals in human physiology," Scientific American, vol. 262, pp. 42-49, 1990.

[9]H. E. Schepers, J. H. G. M. Van Beek and J. B. Bassingthwaighte, "Four methods to estimate the fractal dimension from self-affine signals," IEEE EMB Mag., vol. 12, pp. 57-71, 1992.

[10]P. Bak and K. Chen, "Self-organized criticality," Sci Amer, vol. 246, pp. 46-53, 1991.

[11]C. Tricot, Curves and Fractal Dimension, New York: Springer-Verlag, 1995, vol. 1, page 148-157. 

[12]J. Piskorski and P. Guzik, "Filtering Poincaré plots," Computational Methods in Science and Technology, vol. 11, pp. 39-48, 2005.

[13]P. W. Kamen and A. M. Tonkin, "Application of the Poincaré plot to heart rate variability: a new measure of functional status in heart failure," ANZ J Med, vol. 25, pp. 18–26, 1995.

[14]P. Castiglioni and M. Di Rienzo, "How the threshold R influences approximate entropy analysis of heart rate variability?," Computers in Cardiology, vol. 35, pp. 561−564, 2008.

[15]M. Brennan, M. Palaniswami and P. Kamen, "Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability?" IEEE Trans Biomed Eng., vol. 48, pp. 1342–1347, 2001.

[16]S. Behnia, A. Akhshani, H. Mahmodi and H. Hobbenagi, "On the calculation of chaotic features for nonlinear time series," Chinese J. Physics, vol. 46, pp. 394-404, 2008