ECG beat clustering using fuzzy c-means algorithm and particle swarm optimization

In this paper, an ECG beat clustering method based on fuzzy c-means algorithm and particle swarm optimization is proposed. For this purpose, ECG records which are selected from MIT-BIH arrhythmia database are firstly preprocessed and then four morphological features are extracted for six different types of beats. These features are then clustered with the proposed method. During the classification phase, in order to minimize the incongruity between the experiments and to better evaluate the performance of the proposed system a simple but stable classification method is used. After several experiments it is observed that the proposed method overcomes the restrictions of the fuzzy c-means algorithm which are sensitivity to initialization and trapping into local minima.