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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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Awake Insights for Obstructive Sleep Apnea: Severity Detection Using Tracheal Breathing Sounds and Meta-Model

Ali Mohammad Alqudah1, Zahra Moussavi1,2

  • 1Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.

Diagnostics (Basel, Switzerland)
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a machine learning model using tracheal breathing sounds for obstructive sleep apnea (OSA) severity assessment. The non-invasive system offers a scalable alternative to polysomnography for rapid screening.

Keywords:
OSA severity predictionmeta-modelsobstructive sleep apneatracheal breathing soundswakefulness screening

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Area of Science:

  • Biomedical Engineering
  • Machine Learning Applications
  • Sleep Medicine

Background:

  • Obstructive sleep apnea (OSA) is a common disorder linked to significant cardiovascular and cognitive risks.
  • Current diagnostic methods like polysomnography (PSG) are resource-intensive and unsuitable for widespread, rapid screening.
  • There is a need for efficient, non-invasive tools for accurate OSA severity assessment.

Purpose of the Study:

  • To develop and validate a machine learning (ML) meta-modeling framework for multi-class prediction of obstructive sleep apnea (OSA) severity.
  • To utilize tracheal breathing sounds (TBS) and anthropometric data for a non-invasive OSA assessment.
  • To incorporate conformal prediction for reliable, calibrated confidence sets in OSA severity classification.

Main Methods:

  • A meta-modeling framework was developed, aggregating six one-vs.-one classifiers for multi-class OSA severity prediction.
  • Tracheal breathing sounds (TBS) and anthropometric data were used as input features.
  • Out-of-bag (OOB) estimation, three-fold cross-validation, and conformal prediction were employed to assess generalization and reliability.

Main Results:

  • In a three-class setting (non, mild, moderate/severe), the model achieved 76.7% test accuracy and 77.7% sensitivity.
  • Out-of-bag performance demonstrated high accuracy (91.1%), sensitivity (91.6%), and specificity (95.3%).
  • Conformal prediction provided calibrated confidence sets with full coverage, indicating reliable uncertainty quantification.

Conclusions:

  • The developed non-invasive system shows potential for efficient and rapid assessment of obstructive sleep apnea (OSA) severity while awake.
  • This ML-based approach offers a scalable alternative to polysomnography (PSG) for large-scale screening and clinical triaging.
  • The findings support the utility of tracheal breathing sounds for objective OSA severity classification.