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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

914
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...
914

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Updated: May 1, 2026

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An Automated Algorithm for Obstructive Sleep Apnea Detection Using a Wireless Abdomen-Worn Sensor.

Thi Hang Dang1,2, Seong-Mun Kim1, Min-Seong Choi2

  • 1Department of Electrical Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan 44919, Republic of Korea.

Sensors (Basel, Switzerland)
|April 26, 2025
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Summary

A new wireless wearable sensor effectively screens for obstructive sleep apnea (OSA) using an automated algorithm. This accessible device shows high accuracy for detecting moderate to severe OSA, aiding in diagnosis and follow-up.

Keywords:
MLP-mixerabdomen-worn sensorcapacitive sensorhome sleep apnea testobstructive sleep apnea

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Artificial Intelligence

Background:

  • Obstructive sleep apnea (OSA) is prevalent in older adults and those with cardiovascular issues.
  • Current OSA diagnosis relies on polysomnography or home sleep apnea tests (HSAT).
  • Wireless wearable devices offer a promising avenue for OSA screening and monitoring.

Purpose of the Study:

  • To introduce and evaluate a novel automated algorithm for OSA detection.
  • To assess the performance of a wireless abdomen-worn sensor (Soomirang) for OSA screening.
  • To compare the accuracy of the Soomirang system with a standard HSAT device.

Main Methods:

  • Developed an automated algorithm using abdominal movement and acceleration data from the Soomirang sensor.
  • Employed an MLP-Mixer deep learning model for classifying normal and apnea events.
  • Simultaneously monitored 37 subjects with both Soomirang and an HSAT device.
  • Utilized Pearson correlation and Bland-Altman analyses to compare sleep time (ST) and apnea-hypopnea index (AHI) estimations.

Main Results:

  • The Soomirang system demonstrated high correlation with HSAT for both ST (r=0.9) and AHI (r=0.95).
  • Estimated sleep time showed an average difference of 7.5 minutes.
  • Apnea-hypopnea index showed an average difference of 3.
  • Achieved 97.14% accuracy, 100% sensitivity, and 95.45% specificity for detecting OSA at AHI ≥ 15.

Conclusions:

  • The proposed algorithm and Soomirang device show excellent performance in detecting moderate to severe OSA.
  • The wireless abdomen-worn device offers a simple, accessible, and effective tool for OSA screening and follow-up.
  • This technology has the potential to improve OSA diagnosis accessibility.