A Novel Method of Clustering ECG Arrhythmia data using Robust Spatial Kernel Fuzzy C-Means

Abstract

This paper presents an robust and efficient unsupervised method for ECG arrhythmia classification. The proposed method consists of two steps: feature selection and clustering. In proposed method, initially the input ECG data is fed into feature selection method to reduce the dimensionality. In this paper, three well known feature selection methods i.e., Principal Component Analysis, Linear Discriminant Analysis (LDA) and Regularized Locality Preserving Indexing (RLPI) are employed. Further, the reduced data is clustered using a Robust Spatial Kernel FCM (RSKFCM) method. RSKFCM is variant of FCM method which uses Gaussian kernel as distance metric and incorporates neighborhood information. To evaluate the proposed method the experimentation’s are conducted on UCI arrhythmia dataset and compared the results with traditional FCM and Kernel Fuzzy C-Means (KFCM). Extensive experimental results show that, the proposed RSKFCM method with RLPI feature selection method outperforms other methods in comparison.