Classification of ECG arrhythmias using Type-2 Fuzzy Clustering Neural Network

In this study, Type-2 Fuzzy clustering neural network (T2FCNN) architecture realized for classification of electrocardiography arrhythmias is presented. Type-2 fuzzy clustering neural network is cascade structure formed by clustering and classification stages. In T2FCNN architecture, clustering stage consisted of select best patterns in all patterns that belongs to same class is executed by type-2 fuzzy c-means clustering (T2FCM). The aim of using T2FCM clustering algorithm is to reduce classification error of neural network by optimization of training pattern set. A new training set consisted of cluster centers obtained by type-2 fuzzy c-means clustering algorithm for each class as separately is formed inputs of neural network. Neural network is trained using backpropagation algorithm. Proposed structure is used classification of five ECG signal class composed normal sinus rhythm, sinus bradycardia, sinus arrhythmia, right bundle branch block and left bundle branch block. Data used in this study is obtained from Physionet database, that belongs to MIT-BIH ECG arrhythmia database. In the end of making applications, proposed T2FCNN structure is classified ECG arrhythmias with 99% detection rate.