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[VOTE versus ACLTE: comparison of two snoring noise classifications using machine learning methods].

C Janott1, M Schmitt2, C Heiser3

  • 1Munich School of BioEngineering, Technische Universität München, Boltzmannstraße 11, 85748, Garching, Deutschland. c.janott@gmx.net.

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Summary

Acoustic snoring analysis aids in diagnosing sleep-disordered breathing. Machine classification models show potential but require larger datasets for improved accuracy in identifying snoring causes.

Keywords:
Data analysisDrug induced sleep endoscopyIntrinsic sleep disordersObstructive sleep apneaRespiratory signs and symptoms

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Acoustics

Background:

  • Acoustic snoring sound analysis offers a noninvasive method for diagnosing snoring mechanisms during natural sleep.
  • This technique supports the development and evaluation of classification schemes for diagnostic purposes.

Purpose of the Study:

  • To develop and evaluate machine classification schemes for snoring sounds.
  • To provide meaningful diagnostic support for snoring conditions.

Main Methods:

  • Trained identically structured machine classification systems using two annotated snoring noise databases (s-VOTE and ACLTE).
  • Employed the openSMILE feature extractor with a linear support vector machine for classification.

Main Results:

  • Achieved an unweighted average recall (UAR) of 55.4% for the s-VOTE model and 49.1% for the ACLTE model.
  • Successfully differentiated epiglottic snoring but showed confusion between velar and oropharyngeal snoring types.

Conclusions:

  • Automated acoustic methods show promise in diagnosing sleep-disordered breathing.
  • Limited training dataset size is a key factor affecting recognition performance.