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Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

Dongyeop Kim1, Ji Yong Park2, Young Wook Song2

  • 1Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.

Sleep Medicine
|October 5, 2024
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Summary

Machine learning identified seven electroencephalography (EEG) features that accurately detect obstructive sleep apnea (OSA). These EEG biomarkers show promise for assessing functional changes in OSA patients.

Keywords:
Machine learningMicrostate analysisNetwork analysisObstructive sleep apneaPower spectrum analysisSleep electroencephalography

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Area of Science:

  • Neuroscience
  • Sleep Medicine
  • Computational Biology

Background:

  • Obstructive sleep apnea (OSA) is a prevalent sleep disorder with significant neurophysiological consequences.
  • Current diagnostic methods primarily rely on polysomnography (PSG), but advanced neuroimaging and analysis techniques offer potential for improved characterization.

Purpose of the Study:

  • To investigate the neurophysiological effects of OSA using multi-channel sleep electroencephalography (EEG).
  • To apply machine learning (ML) methods, including power spectral, network, and microstate analyses, to identify EEG features differentiating OSA severity.

Main Methods:

  • Recruited participants with moderate to severe OSA (apnea-hypopnea index [AHI] ≥ 15) and controls (AHI < 15).
  • Conducted overnight polysomnography (PSG) with 19-channel EEG.
  • Utilized power spectral analysis, graph theory-based network analysis, and EEG microstate analysis.
  • Employed ML techniques to identify differentiating EEG features.

Main Results:

  • Seven EEG features demonstrated significant differences between OSA and control groups, achieving 88.3% accuracy, 92% sensitivity, and 84% specificity.
  • Key features included specific theta and gamma power bands, eigenvector centrality, and microstate durations during different sleep stages.
  • These identified EEG features strongly correlated with PSG parameters indicating OSA severity.

Conclusions:

  • ML and diverse EEG analyses effectively classify moderate to severe OSA.
  • EEG holds potential as a non-invasive biomarker for functional brain changes associated with OSA.
  • This approach may enhance the understanding and management of OSA.