A novel approach in ECG beat recognition using adaptive neural fuzzy filter

Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detection has been the subject of considerable research efforts in recent years. Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both of these approaches, and as a result, adaptive neural fuzzy filters (ANFF) [1] have been evolved. This study presents a comparative study of the classification accuracy of ECG signals using (MLP) with back propagation training algorithm, and a new adaptive neural fuzzy filter architecture (ANFF) for early diagnosis of ECG arrhythmia. ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules [1]. In this paper we used an adaptive neural fuzzy filter as an ECG beat classifier. We combined 3 famous wavelet transforms and used them mid 4 the order AR model coefficient as features. Our results suggest that a new proposed classifier (ANFF) with these features can generalize better than ordinary MLP architecture and also learn better and faster. The results of proposed method show high accuracy in ECG beat classification (97.6%) with 100% specificity and high sensitivity.