Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

Blanco Murillo, José Luis; Hernández Gómez, Luis Alfonso; Fernández Pozo, Rubén y Ramos, Daniel (2012). Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech. "Cognitive Computation" ; pp. 1-15. ISSN 1866-9956. https://doi.org/10.1007/s12559-012-9168-x.

Descripción

Título: Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech
Autor/es:
  • Blanco Murillo, José Luis
  • Hernández Gómez, Luis Alfonso
  • Fernández Pozo, Rubén
  • Ramos, Daniel
Tipo de Documento: Artículo
Título de Revista/Publicación: Cognitive Computation
Fecha: Agosto 2012
Materias:
Palabras Clave Informales: Obstructive sleep apnea (OSA), Continuous speech • Sustained speech, Gaussian mixture models (GMMs), Nonlinear analysis, Speech dynamics, Classification and regression tree (CART)
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

We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech.

Más información

ID de Registro: 16765
Identificador DC: http://oa.upm.es/16765/
Identificador OAI: oai:oa.upm.es:16765
Identificador DOI: 10.1007/s12559-012-9168-x
URL Oficial: http://link.springer.com/article/10.1007%2Fs12559-012-9168-x#
Depositado por: Memoria Investigacion
Depositado el: 10 Ago 2013 07:41
Ultima Modificación: 21 Abr 2016 17:07
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