Detection of severe obstructive sleep apnea through voice analysis |
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| Affiliation: | 1. Data and Signal Processing Group, University of Vic – Central University of Catalonia, Sagrada Família 7, 08500 Vic, Spain;2. Universidad de Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas, Spain;3. Cooclea, S.L., Mestre Garriga 10, 08500 Vic, Spain;4. Respiratory Department IRBLleida, Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain;5. CIBERES, ISCIII, Sinesio Delgado 20, Madrid, Spain;6. Sleep Unit, Service of Pneumology, Hospital Txagorritxu, Servicio Vasco de Salud-Osakidetza, José Achótegui s/n, Vitoria-Gasteiz, Spain;7. Respiratory Department, Sleep Unit – Hospital Universitario Marqués de Valdecilla, Avda. Valdecilla n° 25, 39008 Santander, Spain;8. Respiratory Department, Sleep Unit – Hospital Universitario de Cruces, Plaza de Cruces, s/n 48903 Barakaldo, Bizkaia, Spain;1. Principal Engineer, DECD, CDAC, A-34, Industrial Area, Phase-8, Mohali, India;2. Department of ECE, National Institute of Technology, Kurukshetra, Haryana, India;1. Department of Electrical Engineering, COMSATS Institute of IT, Wah Campus, Wah, Pakistan;2. Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Canada;3. School of Engineering Science at Simon Fraser University, Burnaby, BC V5A 1S6, Canada;1. Departamento de Matemática Aplicada, Escuela Superior de Informática, Universidad de Castilla la Mancha, 28012 Ciudad Real, Spain;2. Departamento de Matemática Aplicada a los Recursos Naturales, E.T. Superior de Ingenieros de Montes, Universidad Politécnica de Madrid, 28040 Madrid, Spain |
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| Abstract: | This 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. |
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| Keywords: | Obstructive sleep apnea Voice processing Genetic algorithms Feature reduction |
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