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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.
The condition is more prevalent among...
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Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor.

Urtnasan Erdenebayar1, Jong Uk Park1, Pilsoo Jeong1

  • 1Department of Biomedical Engineering, School of Health Science, Yonsei University, Wonju, Korea.

Journal of Korean Medical Science
|May 9, 2017
PubMed
Summary

A new method uses a piezo-electric sensor to detect obstructive sleep apnea (OSA), a common but often undiagnosed sleep disorder. This approach simplifies screening by analyzing snoring and heart rate variability for improved OSA diagnosis.

Keywords:
Obstructive Sleep ApneaPiezo-Electric SensorPulse Rate VariabilitySnoring IndexSupport Vector Machine

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Signal Processing

Background:

  • Obstructive sleep apnea (OSA) is a prevalent sleep disorder affecting millions globally.
  • A significant majority of OSA cases remain undiagnosed due to complex and costly diagnostic procedures.
  • There is a critical need for simplified, accessible screening tools for OSA detection.

Purpose of the Study:

  • To develop and validate a novel method for obstructive sleep apnea (OSA) detection.
  • To assess the feasibility of using a single-channel piezo-electric sensor for OSA screening.
  • To simplify the diagnostic process for OSA by utilizing readily available physiological signals.

Main Methods:

  • A piezo-electric sensor was employed to simultaneously capture snoring and heartbeat signals.
  • Snoring index (SI) and pulse rate variability (PRV) features were extracted from the sensor data.
  • A support vector machine (SVM) classifier was utilized for automated OSA event detection.

Main Results:

  • The proposed method demonstrated promising performance across different OSA severity groups.
  • For mild OSA, the method achieved a mean accuracy of 71.5% (sensitivity 72.5%, specificity 74.2%).
  • For moderate OSA, mean accuracy was 80.0% (sensitivity 85.8%, specificity 80.5%), and for severe OSA, 71.9% (sensitivity 70.3%, specificity 77.1%).

Conclusions:

  • The study confirms the feasibility of using a piezo-electric sensor for effective OSA detection.
  • This novel approach offers a simplified and potentially more accessible method for OSA screening and monitoring.
  • The findings highlight the potential of this technology for improving OSA diagnosis and patient management.