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Unsupervised Analysis of Arrhythmias using K-means Clustering

The Electrocardiogram provides the valuable information regarding the cardiovascular diseases. Various methods for classification of arrhythmias have been developed by researchers. Classification can be supervised or unsupervised. Various clustering techniques have been used for arrhythmias under the unsupervised category. Clustering has been advisable technique for analysis and interpretation of long term ECG Holter records. In this paper, K-means clustering has been used. The K-means with Squared Euclidean distance has been used for the analysis. Data sets with four types of arrhythmias have been made using MIT-BIH data bases and after applying kmeans using Euclidean with ‘sample’ as seed has been used. The data is classified into five arrhythmia beats type i.e. Normal(N), Premature ventricular contraction (PVC), Paced beats(P), Left Bundle Branch Block(LBBB) and Right Bundle Branch Block(RBBB).