Classification of arrhythmias using spectral features with K Nearest Neighbor method

In this work, five types of arrhythmias observed in electrocardiograph (ECG) signals are analyzed by using their spectral features. K-Nearest Neighbor (KNN) method is used as the classifier. The frequency spectrum of the samples are divided into a variable number of distinct bands and average band powers are used as the feature vectors. The performance of the classifier is tested by changing the width of the frequency bands, the number of neighbors and distance metric. The results are examined based on the average sensitivity, specificity, selectivity and accuracy values. The results show that the optimal KNN classifier is the one which uses 1 nearest neighbor, cityblock distance metric and 0.7 Hz width frequency band.