A New Two-Type Fuzzy C-Means Clustering Algorithm for the Diagnosis of Ventricular Premature Beats

In this paper, on the basis of the type ii fuzzy set to join confirm degrees, to simplify the form, further design type 2 fuzzy c-means (FCM) clustering algorithm, the iteration formula is derived, MIT-BIH ecg data in the database will download subtracted contains no candidate of P wave and the algorithm for these candidate segment analysis, ventricular premature beat signal and normal ecg signals. The experimental results show that the addition of confirmation degree can identify the abnormal points with high accuracy, which can effectively improve the problem that the clustering center of the two-type fuzzy c-means clustering algorithm is not accurate enough, and the diagnostic accuracy of the algorithm can reach 97.56%.