DSpace Repository

Detection of severe obstructive sleep apnea through voice analysis

Show simple item record

dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor Universitat de Vic. Grup de Recerca en Tecnologies Digitals
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Munteanu, C.
dc.contributor.author Martin, O.C.
dc.contributor.author Barbé, F.
dc.contributor.author Queipo, C.
dc.contributor.author Amilibia, J.
dc.contributor.author Durán-Cantolla, J.
dc.date.accessioned 2014-09-17T10:43:30Z
dc.date.available 2014-09-17T10:43:30Z
dc.date.created 2014
dc.date.issued 2014
dc.identifier.citation Solé-Casals, J., Munteanu, C., Martín, O. C., Barbé, F., Queipo, C., Amilibia, J., et al. (2014). Detection of severe obstructive sleep apnea through voice analysis. Applied Soft Computing, 23(0), 346-354.10.1016/j.asoc.2014.06.017 ca_ES
dc.identifier.issn 1568-4946
dc.identifier.uri http://hdl.handle.net/10854/3266
dc.description.abstract tThis paper deals with the potential and limitations of using voice and speech processing to detect Obstruc-tive Sleep Apnea (OSA). An extensive body of voice features has been extracted from patients whopresent various degrees of OSA as well as healthy controls. We analyse the utility of a reduced set offeatures for detecting OSA. We apply various feature selection and reduction schemes (statistical rank-ing, Genetic Algorithms, PCA, LDA) and compare various classifiers (Bayesian Classifiers, kNN, SupportVector Machines, neural networks, Adaboost). S-fold crossvalidation performed on 248 subjects showsthat in the extreme cases (that is, 127 controls and 121 patients with severe OSA) voice alone is able todiscriminate quite well between the presence and absence of OSA. However, this is not the case withmild OSA and healthy snoring patients where voice seems to play a secondary role. We found that thebest classification schemes are achieved using a Genetic Algorithm for feature selection/reduction. en
dc.format application/pdf
dc.format.extent 9 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher Elsevier
dc.rights (c) 2012 Elsevier. Published article is available at: http://dx.doi.org/10.1016/j.asoc.2014.06.017
dc.rights Tots els drets reservats ca_ES
dc.subject.other Veu, Processament de ca_ES
dc.subject.other Apnea del son, Síndrome de l' ca_ES
dc.title Detection of severe obstructive sleep apnea through voice analysis en
dc.type info:eu-repo/semantics/article ca_ES
dc.identifier.doi https://doi.org/10.1016/j.asoc.2014.06.017
dc.relation.publisherversion http://www.sciencedirect.com/science/article/pii/S1568494614002816
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES
dc.type.version info:eu-repo/acceptedVersion ca_ES
dc.indexacio Indexat a WOS/JCR
dc.indexacio Indexat a SCOPUS ca_ES

Files in this item

Show simple item record

Search RIUVic

Advanced Search