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Related Experiment Video

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Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
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Automatic detection of obstructive sleep apnea using speech signals.

Evgenia Goldshtein1, Ariel Tarasiuk, Yaniv Zigel

  • 1BlueLibris, Inc., Menlo Park, CA 94026, USA. evgeniag@bgu.ac.il

IEEE Transactions on Bio-Medical Engineering
|December 22, 2010
PubMed
Summary

Acoustic features in speech can help detect obstructive sleep apnea (OSA). This study found distinct speech patterns in OSA patients, paving the way for a new screening tool.

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Area of Science:

  • Medical Diagnostics
  • Speech Signal Processing
  • Sleep Medicine

Background:

  • Obstructive sleep apnea (OSA) affects 5% of the population, often linked to upper airway anatomical issues.
  • Acoustic speech parameters may reflect vocal tract and soft tissue characteristics relevant to OSA.

Purpose of the Study:

  • To investigate if acoustic speech features differ between obstructive sleep apnea (OSA) patients and non-OSA controls.
  • To develop a classification system for OSA detection using speech signal analysis.

Main Methods:

  • Analyzed acoustic speech features from 93 subjects using text-dependent recordings prior to polysomnography.
  • Employed a Gaussian mixture model with selected features like vocal tract length and linear prediction coefficients.
  • Utilized feature selection techniques to identify discriminative acoustic parameters.

Main Results:

  • Achieved 83% specificity and 79% sensitivity for male OSA patients.
  • Achieved 86% specificity and 84% sensitivity for female OSA patients.
  • Demonstrated significant differences in speech signal properties between OSA and non-OSA groups.

Conclusions:

  • Acoustic features from wakefulness speech signals can effectively detect OSA patients.
  • The developed system shows good specificity and sensitivity for OSA screening.
  • This approach can form the basis for a non-invasive OSA screening tool.