http://www.iaeme.com/IJECET/index.asp 60 editor@iaeme.com International Journal of Electronics and Communication Engineering & Technology (IJECET) Volume 7, Issue 2, March-April 2016, pp. 60–70, Article ID: IJECET_07_02_008 Available online at http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=7&IType=2 Journal Impact Factor (2016): 8.2691 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6464 and ISSN Online: 0976-6472 © IAEME Publication PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA CLASSIFICATION Yedukondalu Kamatham and Nasreen Sultana Dept of ECE, Bhoj Reddy Engineering College, Vinay Nagar, Santhosh Nagar, Saidabad, Hyderabad-500059, India ABSTRACT In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification. Index Terms: Accuracy, Classification, ECG, KNN, MSVM, NBC, Precision and Sensitivity. Cite this Article: Yedukondalu Kamatham and Nasreen Sultana. Performance Evaluation of Artificial Neural Networks for Cardiac Arrhythmia Classification, International Journal of Electronics and Communication Engineering & Technology, 7(2), 2016, pp. 60–70. http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=7&IType=2 1. INTRODUCTION In ECG signals processing, an increasing tremendous improvement have been noticed. The most important diagnosis tool for assessing proper functioning of heart is a bio-electric signal called as Electrocardiogram (ECG), which represents the