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

Blanco Murillo, José Luis and Hernández Gómez, Luis Alfonso and Fernández Pozo, Rubén and 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.

Description

Title: Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech
Author/s:
  • Blanco Murillo, José Luis
  • Hernández Gómez, Luis Alfonso
  • Fernández Pozo, Rubén
  • Ramos, Daniel
Item Type: Article
Título de Revista/Publicación: Cognitive Computation
Date: August 2012
ISSN: 1866-9956
Subjects:
Freetext Keywords: Obstructive sleep apnea (OSA), Continuous speech • Sustained speech, Gaussian mixture models (GMMs), Nonlinear analysis, Speech dynamics, Classification and regression tree (CART)
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

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.

More information

Item ID: 16765
DC Identifier: http://oa.upm.es/16765/
OAI Identifier: oai:oa.upm.es:16765
DOI: 10.1007/s12559-012-9168-x
Official URL: http://link.springer.com/article/10.1007%2Fs12559-012-9168-x#
Deposited by: Memoria Investigacion
Deposited on: 10 Aug 2013 07:41
Last Modified: 21 Apr 2016 17:07
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