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

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

Descripción

Título: GMM-based classifiers for the automatic detection of obstructive sleep apnea
Autor/es:
  • Gómez García, J.A.
  • Blanco Murillo, José Luis
  • Godino Llorente, Juan Ignacio
  • Hernández Gómez, Luis Alfonso
  • Castellanos Domínguez, César Germán
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013)
Fechas del Evento: 11/02/2103 - 14/02/2013
Lugar del Evento: Barcelona, Spain
Título del Libro: 6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013)
Fecha: 2013
Materias:
Palabras Clave Informales: GMM, Supervector, GSV, Nuisance Attribute Projection, Pattern Recognition
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 28944
Identificador DC: http://oa.upm.es/28944/
Identificador OAI: oai:oa.upm.es:28944
Depositado por: Memoria Investigacion
Depositado el: 09 Jul 2014 17:45
Ultima Modificación: 22 Sep 2014 11:43
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