ICTACT Journal on Soft Computing

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Heart Rate Variability Classification Using SADE-ELM Classifier with Bat Feature Selection


Affiliations
1 Department of Computer Science, PSGR Krishnammal College for Women, India
     

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The electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient's cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG) impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient's body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV) is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self- Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN) to be able to increase the accuracy of the effect for HRV transmission classification. Hence, it is observed that the experiment carried out unveils that the precision is improved by the SADE-ELM method and concurrently optimizes the computation time.

Keywords

Self-adaptive Differential Evolution, Extreme Learning Machine, Bat Algorithm, Efficient Fuzzy Kohonen Clustering Network.
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  • R. Chary et.al., “Classification of Cardiac Abnormalities using Heart Rate Signals”, Medical and Biological Engineering and Computing, Vol. 42, No. 3, pp. 288-293, 2005.

  • M.H. Song et.al., “Support Vector Machine Based Arrhythmia Classification using Reduced Features”, International Journal of Control, Automation and Systems, Vol. 3, No. 4, pp. 571-579, 2005.

  • M.G. Tsipouras, Y. Goletsis and D.I. Fotiadis, “A Method for Arrhythmic Episode Classification in ECGs using Fuzzy Logic and Markov Models”, Computers in Cardiology, pp.361-364, 2004.

  • M.G. Tsipouras, D.I. Fotiadis and D. Sideris, “An Arrhythmia Classification System based on the RR-Interval Signal”, Artificial Intelligence In Medicine, Vol. 33, No. 3, pp. 237-250,2005.

  • Carlos W.D. de Almeida, Renata M.C.R. Souza and Ana Lucia B. Candeias, “IFKCN: Applying Fuzzy Kohonen Clustering Network to Interval Data”, Proceedings of International Joint Conference on Neural Networks, pp. 16, 2012.

  • Jinhui Fan, Songmin Jia and Xiuzhi Li, “The Application of Fuzzy Kohonen Clustering Network for Intelligent Wheelchair Motion Control”, Proceedings of IEEE International Conference on Robotics and Biomimetics, pp.1-6, 2013.

  • J Spilka, V Chudacek, J Kuzilek, L Lhotska and M Hanuliak, “Detection of Inferior Myocardial Infarction: A Comparison of Various Decision Systems and Learning Algorithms”, Computing in Cardiology, pp. 273-276, 2010.

  • H.K. Chatterjee, R. Gupta and M. Mitra, “A Statistical Approach for Determination of Time Plane Features from Digitized ECG”, Computers in Biology and Medicine, Vol.41, No. 5, pp. 278-284, 2011.

  • Iztok Fister, Xin-She Yang, Simon Fong and Yan Zhuang, “Bat algorithm: Recent advances”, Proceedings of IEEE 15th International Symposium on Computational Intelligence and Informatics, pp. 163-167, 2014.

  • Swati Banerjee and Madhuchhanda Mitra, “Classification of ST and Q Type MI Variant using Thresholding and Neighbourhood Estimation Method after Cross Wavelet based Analysis”, Cornell University Library, pp. 1-14, 2013.

  • Li Sun, Yanping Lu, Kaitao Yang and Shaozi Li, “ECG Analysis using Multiple Instance Learning for Myocardial Infarction Detection”, IEEE Transactions on Biomedical Engineering, Vol. 59, No. 12, pp. 3348-3356, 2012.

  • Marco Dorigo, Mauro Birattari and Thomas Stutzle, “Ant Colony Optimization”, IEEE Computational Intelligence Magazine, Vol. 1, No. 4, pp. 28-39, 2006.

  • James Kennedy, “Particle Swarm Optimization”, Springer, 2010.

  • Padmavathi Kora and Sri Ramakrishna Kalva, “Hybrid Bacterial Foraging and Particle Swarm Optimization for Detecting Bundle Branch Block”, SpringerPlus, Vol. 4, pp 1-19, 2015.

  • Xin-She Yang, “A New Metaheuristic Bat-Inspired Algorithm”, Proceedings of Nature Inspired Cooperative Strategies for Optimization, Vol. 284, pp. 65-74, 2010.

  • Xin-She Yang and Xingshi He, “Bat Algorithm: Literature Review and Applications”, International Journal of BioInspired Computation, Vol. 5, No. 3, pp.141-149, 2013.

  • Iztok Fister Jr, et al., “Particle Swarm Optimization for Automatic Creation of Complex Graphic Characters”, Chaos, Solitons and Fractals, Vol. 73, pp. 29-35, 2015.

  • Xin-She Yang, “Bat Algorithm for Multi-Objective Optimisation”, International Journal of Bio-Inspired Computation, Vol. 3, No. 5, pp. 267-274, 2011.


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  • Heart Rate Variability Classification Using SADE-ELM Classifier with Bat Feature Selection

Abstract Views: 308  |  PDF Views: 4

Authors

R. Kavitha
Department of Computer Science, PSGR Krishnammal College for Women, India
T. Christopher
Department of Computer Science, PSGR Krishnammal College for Women, India

Abstract


The electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient's cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG) impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient's body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV) is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self- Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN) to be able to increase the accuracy of the effect for HRV transmission classification. Hence, it is observed that the experiment carried out unveils that the precision is improved by the SADE-ELM method and concurrently optimizes the computation time.

Keywords


Self-adaptive Differential Evolution, Extreme Learning Machine, Bat Algorithm, Efficient Fuzzy Kohonen Clustering Network.

References