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Authors: J.-A. Gómez-García 1 ; J.-L. Blanco-Murillo 1 ; J.-I. Godino-Llorente 1 ; L. A. Hernández Gómez 1 and G. Castellanos-Domínguez 2

Affiliations: 1 Universidad Politécnica de Madrid, Spain ; 2 Procesamiento y Reconocimiento de Señal group (PRS), Colombia

ISBN: 978-989-8565-36-5

Keyword(s): GMM, Supervector, GMM-SVM, Obstructive Sleep Apnea, OSA.

Related Ontology Subjects/Areas/Topics: Acoustic Signal Processing ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Detection and Identification ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: The aim of automatic pathological voice detection systems is to support a more objective, less invasive diagnosis of diseases. Those detection systems mostly employ an optimized representation of the spectral envelope; whereas for classification, Gaussian Mixture Models are typically used. However, the study of Gaussian Mixture Models-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered in pathology detection tasks. The present work aims at considering whether such tools might improve system performance in detection of pathologies, particularly for the Obstructive Sleep Apnea. Having this in mind, the present paper employs Linear Prediction Coding Coefficients, in conjunction with Gaussian Mixture Model-based classifiers for the detection of Obstructive Sleep Apnea, in a database containing the sustained phonation of vowel /a/. The obtained results demonstrate subtle improvements compared to using ba seline automatic detection system. (More)

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Paper citation in several formats:
Gómez-García, J.; Blanco-Murillo, J.; Godino-Llorente, J.; A. Hernández Gómez, L. and Castellanos-Domínguez, G. (2013). GMM-based Classifiers for the Automatic Detection of Obstructive Sleep Apnea.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 364-367. DOI: 10.5220/0004252503640367

@conference{biosignals13,
author={J.{-}A. Gómez{-}García. and J.{-}L. Blanco{-}Murillo. and J.{-}I. Godino{-}Llorente. and L. A. Hernández Gómez. and G. Castellanos{-}Domínguez.},
title={GMM-based Classifiers for the Automatic Detection of Obstructive Sleep Apnea},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={364-367},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004252503640367},
isbn={978-989-8565-36-5},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - GMM-based Classifiers for the Automatic Detection of Obstructive Sleep Apnea
SN - 978-989-8565-36-5
AU - Gómez-García, J.
AU - Blanco-Murillo, J.
AU - Godino-Llorente, J.
AU - A. Hernández Gómez, L.
AU - Castellanos-Domínguez, G.
PY - 2013
SP - 364
EP - 367
DO - 10.5220/0004252503640367

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