COMPARISON OF NEURAL NETWORK, ANFIS, AND SVM CLASSIFIERS FOR PVC ARRHYTHMIA DETECTION

摘要：

Premature Ventricular Contraction (PVC) beats are of great importance in evaluating and predicting life threatening ventricular arrhythmias. The aim of this study is to improve the diagnosis level of detection of PVC arrhythmia from ECG signals. This improvement is based on an appropriate choice of features for the selected task. We extracted fourteen features including, temporal, morphological features from MIT/BIH ECG signals database and then applying LDA algorithm, we reduced them into nine features. Finally we use a Neural Network, an ANFIS, and a SVM as classifiers. Satisfactory result obtained with accuracy rates of 99.8％ for Neural Network classifier, 94.8673％ for ANFIS classifier, and 97.57 for SVM classifier.

关键词： ECG biological signal processing neural networks ANSIS support vector machine pattern recognition

作者: ALI GHARAVIRI FARAMARZ DEHGHAN MOHAMMAD TESHNELAB HAMID ABRISHAMI MOGHADDAM

作者单位: Biomedical Engineering Group, Electrical Engineering Department, K.N.Toosi University of Technology, Tehran, Iran

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 750-755

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）