Nonparametric density-based clustering for cardiac arrhythmia analysis. Rodríguez-Sotelo, J., L., Peluffo-Ordoñez, D., Cuesta-Frau, D., & Castellanos-Domínguez, G. In Computers in Cardiology, 2009.
Website abstract bibtex 3 downloads 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-α 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.
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abstract = {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-α 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.},
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author = {Rodríguez-Sotelo, Jośe Luis and Peluffo-Ordoñez, D. and Cuesta-Frau, D. and Castellanos-Domínguez, G.},
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Downloads: 3
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