American Journal of Circuits, Systems and Signal Processing
Articles Information
American Journal of Circuits, Systems and Signal Processing, Vol.3, No.3, Jun. 2017, Pub. Date: Dec. 9, 2017
Classification of Electrocardiogram Signal Using Support Vector Machine Based on Fractal Extraction by FD
Pages: 12-22 Views: 1856 Downloads: 652
Authors
[01] Farhana Akter Mou, Department of Electrical & Electronic Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh.
[02] Muhammed Abdullah Al Mahmud, Department of Electrical & Electronic Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh.
[03] A. H. M Zadidul Karim, Department of Electrical & Electronic Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh.
[04] Salma Nazia Rahman, Department of Electrical & Electronic Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh.
[05] Shaikh Rashedur Rahman, Department of Electrical & Electronic Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh.
Abstract
ECG analysis is a major research interest in biomedical signal processing because of the rapid growth in cardiac health care activities all over the world. The rapid progress in computer technology plays an important role in detecting various stages of disease from bio-signals. The assessment process of this technology is more accurate and reliable in clinical therapy. In this paper a method of analysis (ECG) signals using fractal features and support vector machine (SVM) technique has been proposed and some practical experiments have been done to verify this method and it provides a satisfying electronic diagnose paragon for cardiac heart disease so that it can be used by a specialist doctor to diagnose various types of cardiac diseases with an average accuracy of 89.33%. Matter of fact that ECG signals indicate a fractal prototype, it has been tried to discover the fractal dimension (FD) of the ECG time series in a feature extraction phase. Concurrently the Power Spectrum Method (PSM) has been implemented to four kinds of abnormal and normal waveforms. Here PSM can detect abnormal and normal waveforms and all ECG signals has been acquired from the Massachusetts Institute of Technology (BIH) arrhythmia database. Finally, multi-SVM classifier has been constructed and fed by a vector of an ECG signals. The obtained results using SVM classifier confirm the superiority of this classifier for identifying cardiac arrhythmia as compared to conventional one which is analyses of ECG signals based on morphology features and three ECG temporal features, i.e., the complex duration of QRS, the interval between RR and the average interval of RR over last ten beats.
Keywords
Heart, Power Spectrum Method, Support Vector Machine
References
[01] Netter FH (1971), Heart, The Ciba Collection of Medical Illustrations, Ciba Pharmaceutical Company, Summit, N. J, Vol. 5: 293 pp.
[02] Goldman MJ (1986), Principles of Clinical Electrocardiography, Lange Medical Publications, Los Altos, Cal, 12th ed: 460 pp.
[03] Raghav, S.; Mishra, A, K, (2008),” Fractal feature based ECG arrhythmia classification”, TENCON, IEEE Region 10 Conference. 56.
[04] J. M. Blackledge, (2006),”Digital Signal Processing: Mathematical and Computation Methods: Software Development and Applications”, 2nd Edition, London: Horwood Publishing Limited.
[05] S. Z. Mahmoodabadi, A. Ahmadian, and M. D. Abolhasani, (2005), “ECG Feature Extraction using Daubechies Wavelets”, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343.
[06] S. C. Saxena, A. Sharma, and S. C. Chaudhary, (1997.), “Data compression and feature extraction of ECG signals”, International Journal of Systems Science, vol. 28, no. 5:pp. 483-498.
[07] Al Alfi. M, (2014), “Enhanced Automatic Identification of Arrhythmia in Electrocardiogram (ECG) Signals based on Fractal Features and SVM Technique”, Unpublished Master Dissertation, Zarqa Private University, Jordan, Zarqa.
[08] Silipo, (2011), “ECG Feature Extraction Techniques-A Survey Approach”, International Journal of Engineering, Science and Technology, Vol. 3, No. 8:pp. 122-131.
[09] F. Melgani and L. Bruzzone, (Aug. 2004), “Classification of Hyperspectral Remote Sensing Images with Support Vector Machine”, IEEE Trans. Geosci, Remote Sens, vol. 42, no. 8: pp. 1778–1790.
[10] C.-W. Hsu and C.-J. Lin, (Mar. 2002), “A Comparison of Methods for Multiclass Support Vector Machines,” IEEE Trans. Neural Netw., vol. 13, no. 2: pp. 415–425.
[11] V. S. Chouhan, and S. S. Mehta, (2008) “Detection of QRS Complexes in 12 lead ECG using Adaptive Quantized Threshold”, IJCSNS International Journal of Computer Science and Network Security, vol. 8, no. 1.
[12] V. S. Chouhan, and S. S. Mehta, (March, 2007), “Total Removal of Baseline Drift from ECG Signal”, Proceedings of International conference on Computing:Theory and Applications, ICTTA–07:pp. 512-515, ISI.
[13] P. Vanouplines, “Rescaled Range Analysis and the Fractal Dimension of pi”, University Library, Free University Brussels, Pleinlaan 2, 1050 Brussels Belgium.
[14] Mandelbrot, B. B, (1982), “The Fractal Geometry of Nature”, San Francisco.
[15] Angel Chang, (February, 1993)“Fractals in Biological Systems”. 57
[16] Chih-Wei Hsu, Chih-Jen Lin, (2002), “A Comparison of Methods for Multiclas Support Vector Machines”, IEEE Transactions on Neural Networks, vol. 13, No 2.58
[17] Schepers HE, van Beek JHGM, Bassingtwaighte JB, (1992), “Four Methods to Estimate the Fractal Dimension from Selfaffine Signals”, IEEE Engg Me Bio, (6): 57-64.
[18] MIT-BIH Arrhythmia Database from PhysioBank- Physiologic Signal Archives for Biomedical Research. Retrieved 10 March, 2013, from http://www.physionet.org/ -physiobank/database.
[19] Accardo A., Affinito M., Carrozzi M, Bouquet F, (1997) “Use of the Fractal Dimension for the Analysis of Electroencephalographic Time Series”, Biol Cyber, 77:339-350.
[20] Arle J. E. and Simon R. H, (1990) “An Application of Fractal Dimension to the Detection of Transients in the Electroencephalogram Electroencephalogr”, Clin Neurophysiology, 75, 296305.
[21] F. de Chazal and R. B. Reilly, (Dec. 2006), “A Patient Adapting Heart Beat Classifier Using ECG Morphology and Heartbeat Interval Features,” IEEE Trans. Biomed. Eng. Vol. 53, no. 12: pp. 2535–2543.
[22] Song et al, “Support Vector Machine Based Arrhythmia Classification Using Reduced Features”, (December 2005), International Journal of Control, Automation, and Systems, vol. 3, no. 4:pp. 571-579.
[23] Farid Melgani and Yakoub Bazi, (September 2008), “Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization”, IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 5.
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