Document Type : Original/Review Paper

Authors

1 Department of Electrical Engineering,Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran. Iran.

2 Department of Electrical Engineering,Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran. Iran

Abstract

Cardiac Arrhythmias are known as one of the most dangerous cardiac diseases. Applying intelligent algorithms in this area, leads into the reduction of the ECG signal processing time by the physician as well as reducing the probable mistakes caused by fatigue of the specialist. The purpose of this study is to introduce an intelligent algorithm for the separation of three cardiac arrhythmias by using chaos features of ECG signal and combining three types of the most common classifiers in these signal’s processing area. First, ECG signals related to three cardiac arrhythmias of Atrial Fibrillation, Ventricular Tachycardia and Post Supra Ventricular Tachycardia along with the normal cardiac signal from the arrhythmia database of MIT-BIH were gathered. Then, chaos features describing non-linear dynamic of ECG signal were extracted by calculating the Lyapunov exponent values and signal’s fractal dimension. finally, the compound classifier was used by combining of multilayer perceptron neural network, support vector machine network and K-Nearest Neighbor. Obtained results were compared to the classifying method based on features of time-domain and time-frequency domain, as a proof for the efficacy of the chaos features of the ECG signal. Likewise, to evaluate the efficacy of the compound classifier, each network was used both as separately and also as combined and the results were compared. The obtained results showed that Using the chaos features of ECG signal and the compound classifier, can classify cardiac arrhythmias with 99.1% ± 0.2 accuracy and 99.6% ± 0.1 sensitivity and specificity rate of 99.3 % ± 0.1

Keywords

[1] S. Srivasta, and H. Bhardwaj, “ECG Pattern Analysis using Artificial Neural Network,” SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) , Vol. 7, pp. 1-4, May 2020.
 
[2] G. Yao, and X. Mao, “Interpretation of Electrocardiogram Heartbeat by CNN and GRU,” Computational and Mathematical Methods in Medicine, Vol.  2, pp.  1-10, 2021.
 
[3] O. Berenfeld, and J. F. Rodriguez Matas, “Editorial: Atrial Filrillation: Technology for Diagnosis, Monitoring, and traetment,” Frontiers in Physialogy , Vol. 13, pp. 1-4, Feb 2022.
 
[4] W. Schleifer, and K. Sirvathsan, “Ventricular Arrhythmias State of Art,” Cardiol Clin, Vol. 31, pp. 595-605, 2012.
 
[5] N. V. Goonewardane, and Ang S. Kottegoda, “SupperVentricular Tachycardia,” Journal of Arrhythmia, Vol. 35, pp. 641-692, 2019.
 
[6] B. Anuragha, and K. Suresh Kumar, “Classification of cardiac signals using time domain methods,” ARPN Journal of Engineering and Applied Science, Vol. 3, pp. 7-12, 2008.
 
[7] J. Cai, and G. Zhou, “Real-Time Arrhythmia Classification Algorithm using Time-Domain ECG Feature Based on FFNN and CNN,” Mathematival Problems in Engineering, Vol. 2021, pp. 1-17, 2021.
 
[8] H. Gothwal, and S. Kedawat, “Cardiac arrhythmia detection in an ECG beat signal using fast Fourier transform and artificial neural network,”  J. Biomedical Science and Engineering, Vol. 4, pp. 289-296, 2011.
 
[9] C. H. Lin, “Frequency–domain features for ECG discrimination using gray relational analysis-based classifier,” Computer & Mathematics with Applications, Vol. 55, pp. 680-690, 2008.
 
[10] S. Z. Mahmoodabadi, and A. Ahmadian, “ECG feature extraction using Daubechies wavelets,” Proceeding of the fifth IASTED international conference on visualization, imaging and image processing., Puerto Vallarta, Mexico., IVIP. 2005, pp. 343-348.
 
[11] Q. Zhao, and L. Zhan, “ECG feature extraction and classification using wavelet transforms and support vector machines,” International conference on neural networks and brain., ICNN&B. 2005,  pp. 1086-1092.
 
[12] B. A. Sabrine, and A. Taoufik, “Arrhythmia Classification Using Fractal Dimensions and Neyral Networks,” Advances in Intelligent Systems Research, Vol. 175,pp. 182-187, 2021.
 
[13] K. Kiani, and F. Maghsoudi, “Classification of 7 Arrhythmias from ECG using Fractal Dimensions,” J Bioinform Syst Biol, Vol. 2, pp. 053-065, 2019.
 
[14] A. Casaleggio, and A. Corana, “Study of the Lyapunov exponents of ECG signals from MIT-BIH database,” IEEE Conference: Computers in Cardiology., Zaragoza., CinC. 1995, pp. 697-800.
 
[15] O. Gorshkov, and H. Ombao, “Multi-chaotic Analysis of Inter-Beat (R-R) Intervals in Cardiac Signals for Discrimination between Normal and Pathological Classes,” Entripy, Vol. 23, pp. 1-16, 2021.
 
[16] R. Esteller, and G. Vachtsevanos, “A comparison of waveform fractal dimension algorithms,” IEEE Transactions on circuits and systems-Ι: Fundamental theory and applications, Vol. 48, pp. 177-183, 2001.
 
[17] G. Jaiswal, and R. Paul, “Artificial neural network for ECG classification,” Recent Research in Science and Technology, Vol. 6, pp. 36-38, 2014.
 
[18] J. Fornari, and J. Pelaze, “Bundle Branch Blocks Classification via ECG using MLP Neural Networks,” Foundations and Applications of Intelligent Systems, Vol. 213, pp. 547-561, 2014.
 
[19] A. Ebrahimzadeh, and M. Safarnehad, “Classification of ECG signals using Hermite functions and MLP neural networks,” Journal of AI and Data Mining, Vol. 4 (1), pp. 55-65, 2016.
 
[20] A. Sahedin, Ah. Zakernejad, S. Faridi, M. Javadi, and R. Ebrahimpour, “A Trainable Neural Network Ensemble for ECG Beat Classification,” International Journal of Biomedical and Biological Engineering, Vol. 4 (9), pp. 479-485. 2010.
 
[21] M. Engin, and S. Demirag, “Fuzzy-hybrid neural network based ECG beat recognition using three different types of feature sets,” Cardiovascular Engineering: An International Journal, Vol. 3 (2), pp. 71-80, 2013.
 
[22] A. karen, and E. andres, “Classification models for heart disease prediction using feature selection and PCA,” Informatics in Medicine Unlocked, Vol. 19, pp. 1-11, 2021.
 
[23] R. M. Revadas, “Cardiac Arrhythmia Classification using SVM, KNN and NAIVE BAYES Algorythms,” International Research Journal of Engineering and Technology (IRJET), Vol. 5, pp. 3937-3941, May 2021.
 
[24] V. R. Elgin Christo, H. Khanna Nehemiah, B. Minu, and A. Kannan, "Correlation-based Ensemble Feature Selection using Bioinspired Algorithms and Classification using Back-propagation Neural Network," Computational and Mathematical Methods in Medicine, Vol. 7, pp. 1-17,  Sep 2019.
 
[25] N. Kohli, and N. K. Verma, “Arrhythmia classification using SVM with selected features,” International Journal of Engineering, Science and Technology, Vol.  3, pp. 122-131, 2011.
 
[26] L. I. Kuncheva, and J. C. Bezdek, “Decision Templates for Multiple Classifier Fusion: An Experimental Comparison,” Pattern Recognition, Vol. 34, pp. 299-314, 2001.
 
[27] G. B. Moody, and R. G. Mark, “The impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine, Vol.  20, pp. 45-50, 2001.
 
[28] MIT-BIH Arrhythmia PhysioNet, Available: https://www.physionet.org/physiobank/database.
 
[29] H. D. Abarbanel, and R. Brown, “Lyapunov exponents in chaotic systems: their importance ad their evaluation using observed data,” International journal of modern physics B, Vol. 5, pp. 1347-1375, 1991.
 
[30] J. P. Eckmann, and S. O. Kamphorst, “Lyapunov exponents from time series,” Physical Review A, Vol. 34, pp. 4971-4979, 1986.
 
[31] M. Sano, and Y. Sawada, “Measurement of the Lyapunov spectrum from a chaotic time series,” Physical Review Letters, Vol. 55, pp. 1082-1085, 1988.
 
[32] YS. Hung, and CY Suen, “The behavior – knowledge Space method for Combination of Multi Classifiers,” IEEE Society Conference on Computer Vision and Pattern Recognition., New York., CVPR. 1993, pp. 347-387.
 
[33] I. Kaur, R. Rajini, and A. Marwaha, “ECG Signal Analysis and Arrhythmia Detection Using Wavelet Transform,” J. Inst. Eng. India Ser. B., Vol. 97, pp. 499-507, Dec. 2016.
 
[34] J. Pan, and W. J. Tompkins, “A real time QRS detection algorithm,” IEEE Transaction on Biomedical Engineering., Vol. 32, pp. 230-236, 1985.