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dc.contributor.authorSolé-Casals, Jordi
dc.contributor.authorMunteanu, Cristian
dc.contributor.authorCapdevila Martín, Oriol
dc.contributor.authorBarbé Illa, Ferran
dc.contributor.authorDurán-Cantolla, Joaquín
dc.contributor.authorQueipo, Carlos
dc.contributor.authorAmilibia, Jose
dc.description.abstractThis paper deals with the potential and limitations of using voice and speech processing to detect Obstructive Sleep Apnea (OSA). An extensive body of voice features has been extracted from patients who present various degrees of OSA as well as healthy controls. We analyse the utility of a reduced set of features for detecting OSA. We apply various feature selection and reduction schemes (statistical ranking, Genetic Algorithms, PCA, LDA) and compare various classifiers (Bayesian Classifiers, kNN, Support Vector Machines, neural networks, Adaboost). S-fold crossvalidation performed on 248 subjects shows that in the extreme cases (that is, 127 controls and 121 patients with severe OSA) voice alone is able to discriminate quite well between the presence and absence of OSA. However, this is not the case with mild OSA and healthy snoring patients where voice seems to play a secondary role. We found that the best classification schemes are achieved using a Genetic Algorithm for feature selection/reduction.ca_ES
dc.relation.isformatofVersió postprint del document publicat a
dc.relation.ispartofApplied Soft Computing, 2014, vol. 23, p. 346-354ca_ES
dc.rightscc-by-nc-nd (c) Elsevier, 2014ca_ES
dc.subjectObstructive Sleep Apneaca_ES
dc.subjectVoice processingca_ES
dc.subjectGenetic Algorithmsca_ES
dc.subjectFeature reductionca_ES
dc.titleDetection of Severe Obstructive Sleep Apnea through voice analysisca_ES

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cc-by-nc-nd (c) Elsevier, 2014
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Elsevier, 2014