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Detection of Some Major Heart Diseases Using Fractal Analysis
Nahina Islam, Nafiz Imtiaz Bin Hamid, Adnan Mahmud, Sk.M. Rahman, Arafat H. Khan
Pages - 63 - 70     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
Rescaled Range Analysis,, PVC, APB, LBBB
This paper presents a new method to analyze three specific heart diseases namely Atrial Premature Beat(APB), Left Bundle Branch Block (LBBB) and Premature Ventricular Contraction (PVC). The problem is introduced from the discussion of Fractal Dimension. Further, the fractal dimension is used to distinguish between the Electrocardiogram (ECG) signals of healthy person and persons with PVC, LBBB and APB from the raw ECG data. The work done in this paper can be divided into few steps. First step is the determination of the rescaled range of an ECG signal. Then there comes the necessity of calculating the slope of the rescaled range curve. Through this methodology we have established a range of fractal dimension for healthy person and persons with various heart diseases. The way towards determining the range of fractal dimension for those ECG data taken from MIT-BIH Arrhythmia Database has been explained. Again, the obtained range of fractal dimension is also presented here in a tabular fashion with proper analysis.
CITED BY (6)  
1 Thirugnanam, M., & Anouncia, S. M. (2014). An integrated approach for feature extraction and defect detection in industrial radiographic images–case study on welding defects. International Journal of Industrial and Systems Engineering, 17(4), 424-448.
2 Sedjelmaci, I., & Bereksi-Reguig, F. (2014). fractal analysis of the electrocardiogram signal. Journal of Mechanics in Medicine and Biology, 14(04), 1450055.
3 Agrawal, S., & Gupta, A. (2013). Fractal and EMD based removal of baseline wander and powerline interference from ECG signals. Computers in biology and medicine, 43(11), 1889-1899.
4 Sankhari, S. (2013). Acquisition, Processing, Coding & Study of Fractal Behaviour of ECG Signals (Doctoral dissertation, School of Bioscience and Engineering, Jadavpur University, Kolkata).
5 Sedjelmaci, I., & Reguig, F. B. Etude du Comportement Chaotique et l'Organisation Fractale du Signal ECG: état de l’art.
6 Sedielmaci, I., & Bereksi Reguig, F. (2013, May). Detection of some heart diseases using fractal dimension and chaos theory. In Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on (pp. 89-94). IEEE.
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1 Malvin C. Teich, “Fractal behavior of the Electrocardiogram: Distinguishing Heart Failure and Normal Patients using Wavelet Analysis.”, IEEE-EMBS-18-1128-1996.pdf
2 “Fractals and Fractal Dimension”, http://www.vanderbilt.edu/AnS/psychology/cogsci/chaos/workshop/Fractals.html
3 P. Vanouplines,“Rescaled range analysis and the fractal dimension of pi”. University Library, Free University Brussels, Pleinlaan 2, 1050 Brussels, Belgium.
4 Daoming Zhang, Guojun Tan, Jifei Hao,“Fractal random walk and classification of ECG signal”
5 M. Bodruzzaman,J. Cadzow, R. Shiavi, A. IGlroy, B. Dawant and M. Wilkes,"Hurst’s Rescaled-Range (R/S) Analysis and Fractal Dimension of Electromyographic (EMG) Signal" Southeastcon '91., IEEE Proceedings.
6 MIT-BIH Arrhythmia Database from PhysioBank- physiologic signal archives for biomedical research.
7 Sung-Nien Yu and Ying-Hsiang Chen-"Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network"-ScienceDirect:Pattern Recognition Letters; Volume 28, Issue 10, 15 July 2007, Pages 1142-1150
8 D.Shanthi, Dr.G.Sahoo and Dr.N.Saravanan-"Designing an Artificial Neural Network Model for the Prediction of Thrombo-embolic Stroke"- International Journals of Biometric and Bioinformatics (IJBB): Volume (3), Issue (1)
9 Jinkwon Kim, Hang Sik Shin, Kwangsoo Shin and Myoungho Lee-"Robust algorithm for arrhythmia classification in ECG using extreme learning machine"BioMedical Engineering Online,2009.
Miss Nahina Islam
The peoples university of bangladesh - Bangladesh
Mr. Nafiz Imtiaz Bin Hamid
Islamic University of Technology (IUT) - Bangladesh
Mr. Adnan Mahmud
The Peoples University of Bangladesh - Bangladesh
Mr. Sk.M. Rahman
Central Queensland University (CQU), Australia - Australia
Mr. Arafat H. Khan
The Peoples University of Bangladesh - Bangladesh