Arrhythmia Beat Classification Using Pruned Fuzzy K-Nearest Neighbor Classifier

Abstract

In this paper, pruned fuzzy k-nearest neighbor (PFKNN) classifier is proposed to classify different types of arrhythmia beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification which can be very time consuming and requires large storage space. Hence, we have proposed a time efficient pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using the pruning algorithm with Fuzzy KNN, we have achieved beat classification accuracy of 97% and geometric mean of sensitivity is 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used.