Nonparametric density-based clustering for cardiac arrhythmia analysis

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.