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Title:
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Detection of severe obstructive sleep apnea through voice analysis
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Author:
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Solé-Casals, Jordi; Munteanu, C.; Martin, O.C.; Barbé, F.; Queipo, C.; Amilibia, J.; Durán-Cantolla, J.
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Other authors:
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Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals |
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Notes:
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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. |
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Subject(s):
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-Veu, Processament de
-Apnea del son, Síndrome de l' |
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Rights:
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(c) 2012 Elsevier. Published article is available at: http://dx.doi.org/10.1016/j.asoc.2014.06.017
Tots els drets reservats
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Document type:
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Article
info:eu-repo/acceptedVersion |
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Published by:
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Elsevier
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