A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network

Authors:
Rahime Ceylan;Yüksel Özbay;Bekir Karlik
Affiliations:
Department of Electrical and Electronics Engineering, Engineering and Architecture Faculty, Selcuk University, 42075 Konya, Turkey;Department of Electrical and Electronics Engineering, Engineering and Architecture Faculty, Selcuk University, 42075 Konya, Turkey;Department of Computer Engineering, Fatih University, İstanbul, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper presents an improved classifier for automated diagnostic systems of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of a combined Fuzzy Clustering Neural Network Algorithm for Classification of ECG Arrhythmias using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural network. Type-2 fuzzy c-means clustering is used to improve performance of neural network. The aim of improving classifier's performance is to constitute the best classification system with high accuracy rate for ECG beats. Ten types of ECG arrhythmias (normal beat, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter) obtained from MIT-BIH database were analyzed. However, the presented structure was tested by experimental ECG records of 92 patients (40 male and 52 female, average age is 39.75+/-19.06). The classification accuracy of an improved classifier in training and testing, namely Type-2 Fuzzy Clustering Neural Network (T2FCNN), was compared with neural network (NN) and fuzzy clustering neural network (FCNN). In T2FCNN architecture, decision making has two stages: forming of the new training set obtained by selection of the best arrhythmia for each arrhythmia class using T2FCM and classification using neural network trained on the new training set. The results are demonstrated that the proposed diagnostic systems achieved high (99%) accuracy rate.