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  • Research Article
  • Open Access

Relevance Analysis of Stochastic Biosignals for Identification of Pathologies

  • Lina María Sepúlveda-Cano1Email author,
  • Carlos Daniel Acosta-Medina1 and
  • Germán Castellanos-Domínguez1
EURASIP Journal on Advances in Signal Processing20102011:532349

Received: 16 June 2010

Accepted: 3 December 2010

Published: 12 December 2010


This paper presents a complementary study of the methodology for diagnosing of pathologies, based on relevance analysis of stochastic (time-variant) features that are extracted from t-f representations of biosignal recordings. Dimension reduction is carried out by adapting in time commonly used latent variable techniques for a given relevance function, as evaluation measure of time-variant transformation. Examples of both unsupervised and supervised training are deliberated for distinguishing the set of most relevant stochastic features. Besides, two different combining approaches for feature selection are studied. Firstly, when the considered input set comprises a single type of stochastic features, that is, having the same principle of generation. Secondly, when the whole input set of parameters is taken into consideration no matter of their physical meaning. For validation purposes, the methodology is tested for the concrete case of diagnosing of obstructive sleep apnea. Achieved results related to performed accuracy and dimension reduction are comparable with respect to other outcomes reported in the literature, and thus clearly showing that proposed methodology can be focused on finding alternative methods minimizing the parameters used for pathology diagnosing.


Obstructive Sleep ApneaFeature SelectionSleep ApneaQuantum InformationPathology Diagnose

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Authors’ Affiliations

Grupo de Procesamiento y Reconocimiento de Señales, Universidad Nacional de Colombia, Manizales, Colombia


© Lina María Sepúlveda-Cano et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.