Fuzzy Clustering of ECG Beats Using a New Metaheuristic Approach

This study proposes a new single-solution based metaheuristic, namely the Vortex Search algorithm (VS), for fuzzy clustering of ECG beats. The newly proposed metaheuristic is quite simple and highly competitive when compared to the population-based metaheuristics. In order to study the performance of the proposed method a number of experiments are performed over a dataset which is created by using the records selected from MIT-BIH arrhythmia database. The selected records includes six type of beats, namely, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). The records are first preprocessed and then four morphological features are extracted for each beat type to form the training and test sets. By using the newly proposed method, fuzzy cluster centers of the training set is found. By using these clusters' centers a supervised classification method is then classified the test set to evaluate the clustering performance of the method. The results are compared to the fuzzy c-means algorithm (FCM), fuzzy c-means algorithm with particle swarm optimization (FCM-PSO2011) and fuzzy c-means algorithm with artificial bee colony (FCMABC). It is shown that, in spite of its simplicity, the newly proposed metaheuristic with fuzzy c-means algorithm (FCM-VS) is highly competitive and performs quite well when compared to FCM-PSO2011 and FCM-ABC methods.