Fuzzy C-means Clustering, Neural Network, Wt, and Hrv for Classification of Cardiac Arrhythmia

The classification of the electrocardiogram registration into different pathologies diseases devises is a complex pattern recognition task. 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. In this paper, a new cardiology system has been proposed for diagnosis, consultation, and treatment. The aim of this method is to help to practitioner doctor. During the recording of ECG signal, different forms of noise can be superimposed in the useful signal. This model consists of three subsystems. The first subsystem divides into suppression of base line and filtering the ECG recorded from different forms of noise that can be superimposed in the useful signal. The second subsystem realizes the extraction of RR interval using wavelet transform, and pre-classification based on FCMC technique. The third subsystem classifies the output clusters centers of the second using artificial neural network (ANN). In addition, FCMC-HRV is a new method proposed for classification of ECG. In this study, a combined classification system has been designed using fuzzy c-means clustering (FCMC) algorithm and neural networks. FCMC was used to improve performance of neural networks which was obtained very high performance accuracy to classify RR intervals of ECG signals. The ECG signals taken from MIT-BIH ECG database are used in training and testing data to classify four different arrhythmias (Atrial Fibrillation Termination). The test results suggest that HRV-FCMCNN structure can generalize better and is faster than other structures. Correct classification rate was found as 99.99% using proposed combination of Fuzzy CMeans Clustering Neural Networks (FCMCNN) method.