Clustering of ECG Signals Based on Fuzzy Neural Network with Initial Weights Generated by Genetic Algorithm

Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. Whereas clustering of electrocardiogram (ECG) signals will help to identify the heart diseases as soon as possible. In this regard, neural network and fuzzy logic have been used in many application areas while each of them has the advantages and disadvantages. Thus, the present paper utilizes the proposed fuzzy neural network (FNN) with initial weights generated by genetic algorithm (GFNN) for the sake of improvement testing speed, accuracy and for reducing the chance of the FNN getting stuck on a local minimum. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat and atrial fibrillation beat) obtained from the Physio Bank databases were clustered by the proposed GFNN model. Model of evaluation results indicate that the proposed model can perform more accurately and it has less testing speed than the conventional statistical methods, a single ANN and FNN. The total clustering accuracy of the GFNN model is 98.23%.