Nonparametric density-based clustering for cardiac arrhythmia analysis - Rodriguez-Sotelo, J L; Peluffo-Ordoez, D; Cuesta-Frau, D; Castellanos-Dominguez, G (2009 36th Annual Computers in Cardiology Conference (CinC), 2009)
In this work, a nonsupervised algorithm for feature selection and a non-parametric density-based clustering algorithm are presented, whose density estimation is performed by Parzen's window approach; this algorithm solves the problem that individual components of the mixture should be Gaussian. The method is applied to a set of recordings from MIT/BIH's arrhythmia database with five groups of arrhythmias recommended by the AAMI. The heartbeats are characterized using prematurity indices, morphological and representation features, which are selected with the Q-a algorithm. The results are assessed by means supervised (Se, Sp, Sel) and nonsupervised indices for each arrhythmia. The proposed system presents comparable results than other unsupervised methods of literature.