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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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Breathable soft bioelectronics for enhanced automatic detection of obstructive sleep apnea

Seunghyeb Ban1, Youngjin Kwon2, Ikhwan Shin3

  • 1George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA; Wearable Intelligent Systems and Healthcare Center (WISH Center) at the Institute for Matter and Systems, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Biosensors & Bioelectronics
|July 3, 2025
PubMed

Insights

A new wireless wearable device can detect obstructive sleep apnea (OSA) in children using AI. This system offers a comfortable, accessible alternative to traditional sleep studies for diagnosing pediatric OSA.

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Sleep Medicine

Background:

  • Obstructive sleep apnea (OSA) affects 1-5% of children, with higher rates in those with cleft lip/palate and dentofacial deformities.
  • Current diagnostic methods like polysomnography are costly, complex, and uncomfortable, limiting access for pediatric patients.
  • There is a need for accessible, non-invasive diagnostic tools for pediatric OSA.

Purpose of the Study:

  • To introduce a novel wireless, soft, and breathable bioelectronic system for detecting obstructive sleep apnea (OSA) in children.
  • To develop a deep learning framework for automatic sleep stage classification and apnea event detection.
  • To provide a foundation for diagnosing pediatric OSA and monitoring outcomes of orthognathic surgery.

Main Methods:

  • Development of a wearable bioelectronic system with a perforated, deformable structure for improved skin conformity and reduced motion artifacts.
  • Measurement of electrophysiological signals from the face using the wearable device.
  • Application of a deep learning framework combining multi-stream convolutional neural networks and bi-directional long short-term memory models for data analysis.

Main Results:

  • The developed system successfully detects obstructive sleep apnea (OSA) by analyzing electrophysiological signals.
  • The deep learning model accurately classifies sleep stages and identifies apnea events.
  • The wearable device's design minimizes discomfort and motion artifacts, enhancing data quality.

Conclusions:

  • The wireless bioelectronic system presents a promising, accessible solution for diagnosing pediatric obstructive sleep apnea (OSA).
  • This technology has the potential to improve early diagnosis and management of OSA in high-risk pediatric populations.
  • The system could also be valuable for differentiating pre- and post-operative sleep patterns in patients undergoing orthognathic surgery.

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