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Classification of ECG Arrhythmias, Fuzzy C-Means Clustering, Wavelet Transform

The classification of the electrocardiogram registration into different pathologies disease devises is a complex pattern recognition task. In this paper, we propose a generic feature extraction for classification of ECG arrhythmias using a fuzzy c-means (FCM) clustering and Heart Rate variability (HRV). The traditional methods of diagnosis and classification present some inconveniences; seen that the precision of credit note one diagnosis exact depends on the cardiologist experience and the rate concentration. Due to the high mortality rate of heart diseases, early detection and precise discrimination of ECG arrhythmia is essential for the treatment of patients. During the recording of ECG signal, different forms of noise can be superimposed in the useful signal. The pre-treatment of ECG imposes the suppression of these perturbation signals. The row date is preprocessed, normalized and then data points are clustered using FCM technique. In this work, four different structures, FCM-HRV, PCM-HRV, FCMC-HRV and FPCM-HRV are formed by using heart rate variability technique and fuzzy c-means clustering. In addition, FCM-HRV is the new method proposed for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient diagnosis. The ECG signals taken from MIT-BIH ECG database are used in training to classify 4 different arrhythmias (Atrial Fibrillation Termination). All of the structures are tested by using the same ECG records. The test results suggest that FCMC-HRV structure can generalize better and is faster than the other structures.