GMM-based classifiers for the automatic detection of obstructive sleep apnea

Gómez García, J.A. and Blanco Murillo, José Luis and Godino Llorente, Juan Ignacio and Hernández Gómez, Luis Alfonso and Castellanos Domínguez, César Germán (2013). GMM-based classifiers for the automatic detection of obstructive sleep apnea. In: "6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013)", 11/02/2103 - 14/02/2013, Barcelona, Spain. pp. 1-6.

Description

Title: GMM-based classifiers for the automatic detection of obstructive sleep apnea
Author/s:
  • Gómez García, J.A.
  • Blanco Murillo, José Luis
  • Godino Llorente, Juan Ignacio
  • Hernández Gómez, Luis Alfonso
  • Castellanos Domínguez, César Germán
Item Type: Presentation at Congress or Conference (Article)
Event Title: 6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013)
Event Dates: 11/02/2103 - 14/02/2013
Event Location: Barcelona, Spain
Title of Book: 6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013)
Date: 2013
Subjects:
Freetext Keywords: GMM, Supervector, GSV, Nuisance Attribute Projection, Pattern Recognition
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those system include the usage of a optimized representation of the spectral envelope, either based on cepstral coefficients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefficients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefficients) forcharacterization, and Gaussian Mixture Models for posterior classification. However, the study of recently proposed GMM-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered inpathology detection labours. The present work aims at testing whether or not the employment of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specifically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs an Obstructive Sleep Apnea database, in conjunction with GMM-based classifiers looking for a better performance. The results show that an improved performance might be obtained by using such approach.

More information

Item ID: 28944
DC Identifier: http://oa.upm.es/28944/
OAI Identifier: oai:oa.upm.es:28944
Deposited by: Memoria Investigacion
Deposited on: 09 Jul 2014 17:45
Last Modified: 22 Sep 2014 11:43
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