Document Type : Full Research Paper


1 Msc Student, Biomedical Engineering Department, Semnan University, Semnan, Iran

2 Assistant Professor, Biomedical Engineering Department, Semnan University, Semnan, Iran



The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion to evaluate chaotic systems and to quantify the fractal dimensions. Previous studies have shown that classical Hurst index is not appropriate for classification of cardiac arrhythmias because not only selection of algorithm parameters affect the value of determined Hurst index, but also it significantly varies as the heart rate changes. In this paper, modified multiple Hurst index has been proposed to classify the cardiac arrhythmias. The presented index is resistant against changes in heart rate and can be used to identify appropriate features to classify the cardiac arrhythmias. 80 signal from four types of ECG beats obtained from the MIT-BIH Arrhythmia dataset has been used to validate the algorithm. Results show that this method is able to detect normal rhythm and right bundle branch block (RBBB), left bundle branch block (LBBB) and atrial premature complex (APC) arrhythmias with accuracy of 100%, 96.25% and 88.75% using artificialneural network, k nearest neighbor and LDA classifiers respectively.


Main Subjects

[1]           N. Xinbao, B. Chunhua, W. Jun and Y. Chen, “Research progress in nonlinear analysis of heart electric activities” Chinese Science Bulletin, vol. 51, no. 4, pp. 385-393, 2006.
[2]           C.K. Chen, C.L. Lin and Y.M Chiu, “Individual identification based on chaotic electrocardiogram signals” Proc. Conference on Industrial Electronics and Applications, 2011.
[3]           E.D. Obeyli, “Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals”, Expert Systems with Applications, vol. 37, no. 2, pp. 1192-1199, 2010.
[4]           H.B. Xie, Z.M. Gao and H. Liu, “Classification of ventricular tachycardia and fibrillation using fuzzy similarity-based approximate entropy”, Expert Systems with Applications, vol. 38, no. 4, pp. 3973–3981, 2011.
[5]           M. Rhaman, A.Z. Karim, M.M Hasan and J. Sultana, “Successive RR interval analysis of PVC with sinus rhythm using fractal dimension, Poincare plot and sample entropy method”, International Journal of Image, Graphics and Signal Processing, vol. 5, no. 2, 2013.
[6]           A.K. Mishra and S. Raghav, “Local fractal dimension based ECG arrhythmia classification”, Biomedical Signal Processing and Control, vol. 5, no. 2, pp. 114–123, 2010.
[7]           M.L. Talbi, A. Charef, P. Ravier, ”Arrhythmias classification using the fractal behavior of the power spectrum density of the QRS Complex and ANN”, Int. conference on High Performance Computing and Simulation, pp. 399-404, 2010.
[8]           S. Don, D. Chung, D. Min and E. Choi, “Analysis of Electrocardiogram Signals of Arrhythmia and Ischemia using Fractal and Statistical Features” Journal of Mechanics in Medicine and Biology, vol. 13, no. 1, pp. 1350008, 2013.
[9]           Y. Sun, K.L. Chan and S.M. Krishnan, “Life-threatening ventricular arrhythmia recognition by nonlinear descriptor”, Biomedical Engineering Online, vol. 4, no. 1, pp. 1-6, 2005.
[10]         M. Julian, R. Alcaraz and J.J. Rieta, “Generalized hurst exponents as a tool to estimate atrial fibrillation organization from the Surface ECG”, Proc. conference on Computing in Cardiology, pp. 1199-1202, 2013.
[11]         U.R. Acharya, H. Fujita, V.K. Sudarshan, V.S. Sree, L.W.J. Eugene, D.N. Ghista and R. San Tan, “An integrated index for detection of sudden cardiac death using discrete wavelet transform and nonlinear features”, Knowledge-Based Systems, vol. 83, pp. 149–158, 2015.
[12]         M.A. Sanchez Granero, J.E. Trinidad Segovia and J. Garcia Perez, “Some comments on hurst exponent and the long memory processes on capital markets”, Physica A: Statistical Mechanics and its applications, vol. 387, no. 22, pp. 5543-5551, 2008.
[13]         J.B. Bassingthwaighte and G.M. Raymond, “Evaluating rescaled range analysis for time series”, Annals of Biomedical Engineering, vol. 22, no. 4, pp. 432-444, 1994.
[14]         M. Hemmatian and A. Maleki, “Influence of heart rate on improvement of fractal dimension feature based on hurst index method for cardiac arrhythmia classification applications,” Proc. of 2nd International Congress of electrical engineering, computer science and information technology, pp. 265-274, 2015 (In Persian).
 [15]        A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng and H. E. Stanley, “{PhysioBank, PhysioToolkit, and PhysioNet}: Components of a New Research Resource for Complex Physiologic Signals”, Circulation, vol. 101, no. 23, pp. e215-e220, June, 2000.
[16]         R.R. Sarvestani, R. Boostani and M. Roopaei, ”VT and VF classification using trajectory analysis”, Nonlinear Analysis: Theory, Method and Applications, vol. 71, no. 12, pp. 55-61, 2009.
[17]         A. Block, W.V Bloh and H.J. Schellnhuber, “Efficient box-counting determination of generalized fractal dimensions”, Physical Review A, vol. 42, no. 4, pp. 1869-1874, 1990.
[18]         T. Higuchi, “Approach to an irregular time series on the basis of the fractal theory”, Physica D: Nonlinear Phenomena, vol. 31, no. 2, pp. 277-283, 1988.
[19]         S. Spasic, “Spectral and fractal analysis of biosignals and coloured noise”, Symposium on Intelligent Systems and Informatics, Serbia, pp 147-149, 2007.
[20]         Z. Wang, D. Guo, X. Li and Y. Fei, “Estimating hurst exponent with wavelet packet” Proc. Conference on Computer Aided Industrial Design and Conceptual Design, pp. 1-4, 2006.
[21]         E. Molino-Minero-Re, F. Garcia-Nocetti, H. Benitez-Perez, “Application of a Time-Scale Local Hurst Exponent analysis to time series” Digital Signal Processing, vol. 37, pp. 92-99, 2015.
[22]         A.P. Pentland, “Fractal based description of natural scenes”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, pp. 661-674, 1984.
[23]         J. Theiler, S. Eubank, A. Longtin, B. Galdrikian and J.D. Farmer, “Testing for nonlinearity in time series: the method of surrogate data”, Physica D, vol 58, pp 77-94, 1992.