Scholarlink

<p>Obstructive Sleep Apnea (OSA) is traditionally diagnosed via Polysomnography (PSG), which relies on multiple wired sensors in an unfamiliar hospital setting. In this study, a compact home-sleep-test system is proposed, integrating a fringing-field capacitive sensor for wireless respiratory-effort monitoring system(<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{\varnothing}=4cm)\)</EquationSource> </InlineEquation>, and a nasal airflow sensing system (2&#xa0;cm×2&#xa0;cm) with an connected 2&#xa0;cm × 1&#xa0;cm temperature sensor (both wired to the processing unit). A customized signal-processing algorithm was developed to denoise both channels and automatically identify apnea and hypopnea events. Validation with subjects (<i>n</i> = 31) demonstrated performance metrics (SN = 0.846, SP = 0.944, Precision = 0.917, and Accuracy = 0.903, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{R}^{2}=0.92\)</EquationSource> </InlineEquation> ) in classifying OSA severity. By combining novel capacitive fringing-field sensing and temperature-based airflow measurement into a largely wireless wearable, a practical and accurate alternative to traditional PSG for at-home OSA detection is offered.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Clinical study of an integrated sensor system for detection and classification of obstructive sleep apnea (OSA)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

查看全文
  • Seongmun Kim,
  • Thi Hang Dang,
  • Haewan Cho,
  • Sungmin Shin,
  • Seungup Seo,
  • Sanghyun Lee,
  • Hyungki Min,
  • Jagannath Malik,
  • Franklin Bien

摘要

Obstructive Sleep Apnea (OSA) is traditionally diagnosed via Polysomnography (PSG), which relies on multiple wired sensors in an unfamiliar hospital setting. In this study, a compact home-sleep-test system is proposed, integrating a fringing-field capacitive sensor for wireless respiratory-effort monitoring system( \(\:{\varnothing}=4cm)\) , and a nasal airflow sensing system (2 cm×2 cm) with an connected 2 cm × 1 cm temperature sensor (both wired to the processing unit). A customized signal-processing algorithm was developed to denoise both channels and automatically identify apnea and hypopnea events. Validation with subjects (n = 31) demonstrated performance metrics (SN = 0.846, SP = 0.944, Precision = 0.917, and Accuracy = 0.903, \(\:{R}^{2}=0.92\) ) in classifying OSA severity. By combining novel capacitive fringing-field sensing and temperature-based airflow measurement into a largely wireless wearable, a practical and accurate alternative to traditional PSG for at-home OSA detection is offered.