Clustering of arrhythmic ECG beats using morphological properties and windowed raw ECG data

In this study, six types of arrhythmia beats observed in ECG signals have been analysed by using clustering methods. A set of morphological properties and windowed raw ECG data are used as feature vectors in clustering algorithms. Purpose of the analysis is to see if the examined arrhytmia types form natural groups in the feature spaces. The performances of the clustering algorithms are tested by different distance metrics and algorithms. The results are examined based on the average sensitivity, specificity, selectivity and accuracy of the classifier. The results show that k-means clustering technique with the distance parameter set at cosine values by using the windowed raw data features give better results. Results also show that analyzed arrythmia types do not form distinct clusters in examined feature spaces. On the other hand, in some cases very high specificity results are observed for some arrythmia types. That means suggested features could be quite useful in elimination processes in hierarchic classifiers.