Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43079
Title: Cepstrum coefficients of the RR series for the detection of obstructive sleep apnea based on different classifiers
Authors: Ravelo-García, Antonio 
Navarro-Mesa, Juan L. 
Martín-González, Sofía 
Hernández-Pérez, Eduardo 
Quintana-Morales, Pedro 
Guerra-Moreno, Iván 
Navarro-Esteva, Javier
Juliá-Serdá, Gabriel
UNESCO Clasification: 3307 Tecnología electrónica
Issue Date: 2013
Journal: Lecture Notes in Computer Science 
Conference: 14th International Conference on Computer Aided Systems Theory (EUROCAST) 
14th International Conference on Computer Aided Systems Theory, EUROCAST 2013 
Abstract: Two automatic statistical methods for the classification of the obstructive sleep apnoea syndrome based on the cepstrum coefficients of the RR series obtained from the Electrocardiogram (ECG) are presented. We study the effect of working with Linear Discriminant Analysis (LDA) and compare its performance with a reference detector based on Support Vector Machines (SVM). These classifications methods require two previous stages: preprocessing and feature extraction. Firstly, R instants are detected previous to the feature extraction phase thanks to a preprocessing over the ECG. Secondly, Cepstrum Coefficients over the RR signal is applied to extract the relevant characteristics specially those related to the system modelled by the filter-type elements concentrated in the low time lag region.
URI: http://hdl.handle.net/10553/43079
ISBN: 9783642538612
ISSN: 0302-9743
DOI: 10.1007/978-3-642-53862-9-34
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 8112 LNCS, p. 266-271
Appears in Collections:Actas de congresos
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