Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44818, first published .
Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation

Journals

  1. Smith C, Vendrame M. Perspective: A resident’s role in promoting safe machine-learning tools in sleep medicine. Journal of Clinical Sleep Medicine 2023;19(11):1985 View
  2. Kim M, Choi M. STOP-Bang and Smartwatch’s Two-Step Approach for Obstructive Sleep Apnea Screening. Korean Journal of Otorhinolaryngology-Head and Neck Surgery 2023;66(7):455 View
  3. Yoon H, Choi S. Technologies for sleep monitoring at home: wearables and nearables. Biomedical Engineering Letters 2023;13(3):313 View
  4. Han S, Kim D, Rhee C, Cho S, Le V, Cho E, Kim H, Yoon I, Jang H, Hong J, Lee D, Kim J. In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography. JAMA Otolaryngology–Head & Neck Surgery 2024;150(1):22 View
  5. Singtothong C, Siriborvornratanakul T. Deep-learning based sleep apnea detection using sleep sound, SpO2, and pulse rate. International Journal of Information Technology 2024 View