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

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DOI: 10.4236/jbise.2009.22015    6,259 Downloads   15,848 Views   Citations


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.

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The authors declare no conflicts of interest.

Cite this paper

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.


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