Nonparametric density-based clustering for cardiac arrhythmia analysis

• Published in: 2009 36th Annual Computers in Cardiology Conference (CinC) pp. 569 - 572
• Main Authors: Rodriguez-Sotelo, J L, Peluffo-Ordoez, D, Cuesta-Frau, D, Castellanos-Dominguez, G
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2009
• Subjects: Algorithm design and analysis; Clustering algorithms; Computational efficiency; Heart rate variability; Labeling; Laplace equations; Morphology; Partitioning algorithms; Signal analysis; Spatial databases
• ISBN: 9781424472819; 1424472814
• ISSN: 0276-6574

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.