This paper is in the following e-collection/theme issue:

Artificial Intelligence (1602) Novel Sensors and Data Acquisition Methods in Cardiology (209) Machine Learning (1627) Clinical Information and Decision Making (1548) Wearable Devices and Sensors (947)

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

Authors of this article:

Vu Linh Le1 Author Orcid Image ;   Daewoo Kim1 Author Orcid Image ;   Eunsung Cho1 Author Orcid Image ;   Hyeryung Jang2 Author Orcid Image ;   Roben Delos Reyes1 Author Orcid Image ;   Hyunggug Kim1 Author Orcid Image ;   Dongheon Lee1 Author Orcid Image ;   In-Young Yoon3, 4 Author Orcid Image ;   Joonki Hong1 Author Orcid Image ;   Jeong-Whun Kim4, 5 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

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;16(8):4869 View
  6. Lillini D, Aironi C, Migliorelli L, Gabrielli L, Squartini S. SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals. Sensors 2024;24(23):7782 View
  7. Song Y, Ding L, Peng J, Song L, Zhang X. Screening for obstructive sleep apnea hypopnea using sleep breathing sounds based on the PSG-audio dataset. Biomedical Signal Processing and Control 2025;103:107472 View
  8. Giorgi L, Nardelli D, Moffa A, Iafrati F, Di Giovanni S, Olszewska E, Baptista P, Sabatino L, Casale M. Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review. Healthcare 2025;13(2):181 View
  9. Pinilla L, Chai‐Coetzer C, Eckert D. Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics. Respirology 2025 View