Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis.

Journal: Sleep
PMID: 39821673

Abstract

STUDY OBJECTIVES: This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilizes photoplethysmography (red, infrared, and green channels) and accelerometer sensors to assess sleep apnea by the AI system.

Authors

  • Ke-Wei Chen
    PranaQ Pte. Ltd., Singapore.
  • Chun-Hsien Tseng
    Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Hsin-Chien Lee
    Graduate Institute of Humanities in Medicine, College of Humanities & Social Sciences, Taipei Medical University, Taipei, Taiwan.
  • Wen-Te Liu
    Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
  • Kun-Ta Chou
    Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Hau-Tieng Wu
    Mathematics, University of Toronto, Toronto, Ontario, Canada.

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