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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

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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.
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Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
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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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Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning.

Bochun Wang1,2, Xianwen Tang3, Hao Ai3

  • 1Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.

Nature and Science of Sleep
|November 17, 2022
PubMed
Summary

A novel deep learning method using sleep sounds can detect apneic events and identify obstructive sleep apnea (OSA). This noncontact audio approach offers a feasible, comfortable, and low-cost tool for community-based OSA assessment.

Keywords:
deep learningobstructive sleep apneasleep sounds

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Sleep Medicine

Background:

  • Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repeated episodes of airway obstruction.
  • Current diagnostic methods like polysomnography (PSG) can be invasive and costly.
  • There is a need for accessible and noninvasive tools for OSA screening and diagnosis.

Purpose of the Study:

  • To develop and validate a novel deep learning algorithm (OSAnet) for automatic detection of sleep apneic events.
  • To estimate the apnea-hypopnea index (AHI) and identify OSA using only sleep sounds.
  • To assess the feasibility of using noncontact audio recordings for OSA identification.

Main Methods:

  • A cross-sectional study involving participants with habitual snoring or heavy breathing during sleep.
  • Simultaneous recording of sleep sounds and polysomnography (PSG) in a standard room.
  • Development and testing of a deep convolutional neural network (OSAnet) trained on sleep sound data.
  • Performance evaluation using sensitivity, specificity, accuracy, Cohen's kappa, and AUC against PSG as the reference.

Main Results:

  • The OSAnet algorithm achieved a precision of 0.81 and sensitivity of 0.78 for overall sleep event detection in the test group.
  • The algorithm demonstrated high diagnostic performance for severe OSA cases, with 95.6% sensitivity and 91.6% specificity.
  • A total of 135 participants were included, with 59 in the independent test group.

Conclusions:

  • A deep learning algorithm utilizing sleep sounds is a feasible tool for apneic event detection and OSA identification.
  • This noncontact audio-based technique shows promise for comfortable and low-cost OSA assessment in community settings.
  • Further research is warranted to develop and validate this approach for home-based OSA monitoring.