Classification of Heart Rate Data Using BFO-KFCM Clustering and Improved Extreme Learning Machine Classifier

An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. The heart rate varies not only in relation to the cardiac demand but is also affected by the presence of cardiac disease and diabetes. Furthermore, it has been shown that Heart Rate Variability (HRV) may be used as an early indicator of cardiac disease susceptibility and the presence of diabetes. Therefore, the heart rate variability may be used for early clinical screening of these diseases. The generalization performance of the SVM classifier is not sufficient for the correct classification of heart rate data. To overcome this problem the Improved Extreme Learning Machine (IELM) classifier is used which works by searching for the best value of the parameters, and upstream by looking for the best subset of features using Bacterial Foraging Optimization (BFO) that feed the classifier. In this work, nine linear and nonlinear features are extracted from the HRV signals. After the preprocessing, feature extraction is done along with feature selection using BFO for data reduction. Then, proposed a scheme to integrate Kernel Fuzzy C-Means (KFCM) clustering and Classifier to improve the accuracy result for ECG beat classification. The results show that the proposed method is effective for classification of heart rate data, with an acceptable high accuracy.