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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

156
Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
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Sleep-Wake Cycles01:24

Sleep-Wake Cycles

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Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
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Updated: Jul 4, 2025

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review.

Ali Mohammad Alqudah1, Ahmed Elwali2, Brendan Kupiak3

  • 1Biomedical Engineering Program, University of Manitoba, 66 Chancellors Cir, Winnipeg, MB, R3T 2N2, Canada.

Medical & Biological Engineering & Computing
|January 26, 2024
PubMed
Summary

Obstructive sleep apnea (OSA) is underdiagnosed due to costly diagnostics. New research explores wakefulness screening methods and machine learning to improve early detection of this common sleep disorder.

Keywords:
ClassificationHome sleep studyOSAPolysomnographyQuestionnaireScreening wakefulnessSleep apnea

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Area of Science:

  • Sleep Medicine
  • Cardiorespiratory Physiology
  • Biomedical Engineering

Background:

  • Obstructive sleep apnea (OSA) is a prevalent chronic condition impacting 1 billion individuals globally.
  • Underdiagnosis of OSA persists due to the high cost and complexity of traditional in-lab sleep studies.
  • Predicting OSA during wakefulness is challenging due to subtle daytime symptoms like sleepiness.

Purpose of the Study:

  • To review and compare emerging methodologies for screening obstructive sleep apnea (OSA) during wakefulness.
  • To highlight advancements in machine learning for precise analysis of OSA-related data.
  • To provide recommendations for future research directions and study designs in OSA screening.

Main Methods:

  • Comparative review of recent studies on wakefulness-based OSA screening techniques.
  • Analysis of machine learning applications in interpreting physiological data for OSA prediction.
  • Synthesis of findings to identify promising and cost-effective diagnostic approaches.

Main Results:

  • Recent research demonstrates promising quick and accurate methods for OSA prediction during wakefulness.
  • Machine learning algorithms enhance the precision of analyzing physiological data for OSA diagnosis.
  • Wakefulness screening offers a potential solution to overcome limitations of traditional sleep studies.

Conclusions:

  • Developing accessible OSA screening tools for wakefulness is crucial to address underdiagnosis.
  • Machine learning integration is key to advancing the accuracy and efficiency of OSA detection.
  • Further research and refined study designs are needed to validate and implement these novel screening methodologies.