Comparison of similarity measures for clustering electrocardiogram complexes

Abstract

This paper compares four similarity measures such as the city block (L1-norm), the Euclidean (L2-norm), the normalized correlation coefficient, and the simplified gray relational grade for clustering QRS complexes. Performances of the measures include classification accuracy, threshold value selection, noise robustness, and execution time. The clustering algorithm used is the so-called two-step unsupervised method. The best out of the 10 independent runs of the clustering algorithm with randomly selected initial template beat for each run is used to compare the performances of each similarity measure. Simulation results show that the simplified gray relational grade outperforms the other measures