Cognitive Computation

, Volume 5, Issue 4, pp 458–472 | Cite as

Improving Automatic Detection of Obstructive Sleep Apnea Through Nonlinear Analysis of Sustained Speech

  • José Luis Blanco
  • Luis A. Hernández
  • Rubén Fernández
  • Daniel Ramos
Article

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.

Keywords

Obstructive sleep apnea (OSA) Continuous speech Sustained speech Gaussian mixture models (GMMs) Nonlinear analysis Speech dynamics Classification and regression tree (CART) 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • José Luis Blanco
    • 1
  • Luis A. Hernández
    • 1
  • Rubén Fernández
    • 1
  • Daniel Ramos
    • 2
  1. 1.Signal Processing Applications GroupUniversidad Politécnica de Madrid, ETSI de TelecomunicaciónMadridSpain
  2. 2.Biometric Recognition Group (ATVS)Universidad Autónoma de Madrid, Escuela Politécnica SuperiorMadridSpain

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