Author(s)

Glayol Nazari Golpayegani, Amir Homayoun Jafari

Biomedical Engineering Department, Islamic Azad University, Science and Research Branch, Tehran, Iran.
Biomedical Engineering Department, Islamic Azad University, Science and Research Branch, Tehran, Iran..

Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial neural network (ANN) and fuzzy logic approaches are demon-strated to be competent when applied individu-ally 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 architec-ture (ANFF) for early diagnosis of ECG ar-rhythmia. 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 adap-tive 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 archi-tecture and also learn better and faster. The results of proposed method show high accu-racy in ECG beat classification (97.6%) with 100% specificity and high sensitivity.

Adaptive Neural Fuzzy Filter, ECG Arrhythmia Classification, Pattern Recognition, Multilayer Perceptron.

Golpayegani, G. and Jafari, A. (2009) A novel approach in ECG beat recognition using adaptive neural fuzzy filter. Journal of Biomedical Science and Engineering, 2, 80-85. doi: 10.4236/jbise.2009.22015.