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Amelioration of Cardiac Arrythmias Classification Performance Using Artificial Neural Network, Adaptive Neuro-Fuzzy and Fuzzy Inference Systems Classifiers

Authors: Alexandre Boum, Salomon Madinatou


This paper aims at bringing a scientific contribution to the cardiac arrhythmia biomedical diagnosis systems; more precisely to the study of the amelioration of cardiac arrhythmia classification performance using artificial neural network, adaptive neuro-fuzzy and fuzzy inference systems classifiers. The purpose of this amelioration is to enable cardiologists to make reliable diagnosis through automatic cardiac arrhythmia analyzes and classifications based on high confidence classifiers. In this study, six classes of the most commonly encountered arrhythmias are considered: the Right Bundle Branch Block, the Left Bundle Branch Block, the Ventricular Extrasystole, the Auricular Extrasystole, the Atrial Fibrillation and the Normal Cardiac rate beat. From the electrocardiogram (ECG) extracted parameters, we constructed a matrix (360x360) serving as an input data sample for the classifiers based on neural networks and a matrix (1x6) for the classifier based on fuzzy logic. By varying three parameters (the quality of the neural network learning, the data size and the quality of the input parameters) the automatic classification permitted us to obtain the following performances: in terms of correct classification rate, 83.6% was obtained using the fuzzy logic based classifier, 99.7% using the neural network based classifier and 99.8% for the adaptive neuro-fuzzy based classifier. These results are based on signals containing at least 360 cardiac cycles. Based on the comparative analysis of the aforementioned three arrhythmia classifiers, the classifiers based on neural networks exhibit a better performance.

Keywords: Artificial Neural Network, Fuzzy Inference Systems, Adaptive neuro-fuzzy, cardiac arrythmias

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[1] Pratik and V.P Patel, “Classification of ECG Beats based on Fuzzy Inference System “, International Journal of Science, Engineering and Technology Research (IJSETR) Volume 06, Issue 05, pp 835-840, May 2017.
[2] Sarang, “A Survey on ECG Signal Denoising Techniques “, International Conference on Communication Systems and Network Technologies, IEEE, pp 60-64,2013.
[3] Nasser Safdarian, Keivan M. and Nader J.D., “Classification of Cardiac Arrythmias with TSK Fuzzy System Using Genetic Algorithm “, International Journal of Signal Processing. Image Processing and Pattern Recognition, Vol 5 n° 2, pp 89-100, June 2012.
[4] Taiser M.S. and Mohammed « A Study of ECG signals classification using Fuzzy Logic Control », International Journal of Science and Research, pp 374-380, volume 3 Issue 2, February 2014.
[5] Jaylaxmi C. Mannurmath and Prof. Raveenda M., “MATLAB Based ECG Signal Classification “, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014.
[6] N. Kumari, Sunita and Smita, “Comparison of ANNs, Fuzzy Logic and Neuro-Fuzzy Integrated Approach for Diagnosis of Coronary Heart Disease: A Survey “, International Journal of Computer Science and Mobile Computing, Vol.2, Issue 6, pp 216-224, June 2013.
[7] Saad Alshaban and Rawaa Ali “Using Neural and Fuzzy Software for Classification of ECG Signals “, Research Journal of Applied Sciences, Engineering and Technology, pp 5-10, January 05,2010.
[8] J.P. Kelwade and S.S Salankar al, “Prediction of Cardiac Arrhythmia using Artificial Neural Network”, International Journal of Computer Applications (0975 – 8887) Volume 115 No. 20, pp 30-35, April 2015.
[9] R. Acharya, A. Kumar, P.S. Bhat, C.M. Lim, S.S. Lyengar, “Classification of cardiac abnormalities using heart rate signals “, Medical & Biological Engineering & Computing, Vol. 42, pp 288-299, 2004.
[10] Mounia HENDEL, Abdelkader B. and Hiba k. “Classification des Arythmies Cardiaques par les Réseaux des Neurones Artificiels “,5e Conférence Internationale Sciences of electronic, Technologies of Information and Télécommunications, IEEE, SETIT, pp 1-5, 2009.
[11] Ersin Ersoy and Mahmut Hekim, « Feature Extraction Based on Pan Tompkins Algorithm from ECG Signals and Diagnosis of Arrhythmia Using Multilayer Perceptron Neural Network », Journal of New Results in Science, Number 12, pp 205-219 ,2016.
[12] Dr. Rajdeepa B.” Intelligent Classification of ECG Signals Using Adaptive Neuro-Fuzzy Inference System’’ International Journal of Innovative Research in Computer and Communication Engineering, Vol 5, Issue 3, pp 6403-6406, March 2017.
[13] Pramod R. Bokde, “An ECG beat classification using adaptive neuro-fuzzy inference system “, International Research Journal of Advanced Engineering and Science, Volume 2, Issue 2, pp. 354-358, 2017.
[14] Sonal, “ECG Real Time Feature Extraction Using MATLAB”, International Journal of Technology and Science, ISSN (Online) 2350-1111, (Print) 2350-1103 Volume V, Issue 1, pp. 1-4, 2015.
[15] Mayank Kumar and Vinod Kumar, “An Overview of Feature Extraction Techniques of ECG “, American-Eurasian Journal of Scientific Research 12, pp 54-60, 2017.
[16] Gaurav Kumar Jaiswal and Ranbir Paul “ Artificial Neural Network for ecg classification “, Recent Research in Science and Technology, pp 36-38,2014
[17] Taiseer M. and Mohammed A.,’’A study of ECG Signal Classification using fuzzy logic control ‘’International Journal of Science and Research (IJSR), Volume 3 Issue 2, pp 374-380, 2014
[18] Rajendra,’’Classification of heart rate data using artificial neural network and fuzzy equvalence relation’’, Pattern recognition society 36, pp 61-68, 2003
[19] Anuradha and V.C Veera, ‘’Cardiac Arrythmia Classification using fuzzy classifiers’’ Journal of Theoritical and Applied Information Technology, pp 353-359, 2008