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

Artificial Intelligence (2129) Novel Sensors and Data Acquisition Methods in Cardiology (225) Machine Learning (2076) Clinical Information and Decision Making (1915) Wearable Devices and Sensors (1133)

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 (20) 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;30(4):286 View
  10. Zhang Z, Wu W, Fu Z, Han Z, Zhang J, Sun C, Ma D, Wang C. Real-Time Sleep Apnea Detection and Prediction From Single-Channel Airflow. IEEE Transactions on Instrumentation and Measurement 2025;74:1 View
  11. Hayano J, Takeshima M, Imanishi A, Ogasawara M, Yamada Y, Yuda E, Mishima K. Detection of sleep apnea using smartphone-embedded inertial measurement unit. Scientific Reports 2025;15(1) View
  12. Shokouhmand S, Bhatt S, Faezipour M. Artificial Intelligence in Respiratory Health: A Review of AI-Driven Analysis of Oral and Nasal Breathing Sounds for Pulmonary Assessment. Electronics 2025;14(10):1994 View
  13. Tan B, Gao E, Tan N, Yeo B, Tan C, Ng A, Leong Z, Phua C, Uataya M, Goh L, Ong T, Leow L, Huang G, Toh S. Machine Listening for OSA Diagnosis. CHEST 2025;168(2):520 View
  14. Frija J, Millet J, Béquignon E, Covali A, Cathelain G, Houenou J, Benzaquen H, Geoffroy P, Bacry E, Grajoszex M, d’Ortho M. Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity. Sleep and Breathing 2025;29(5) View
  15. Kim Y, Kim M, Shin J, Ko M. ApneaWhisper: Transformer-Based Audio Segmentation for Fine-Grained Non-Invasive Sleep Apnea Detection. Nature and Science of Sleep 2025;Volume 17:2455 View
  16. Park T, Yoon S, Yoon H, Lee K. Sleep Apnea Detection Using Respiratory Sound Data Based on Deep Learning Models. Journal of information and communication convergence engineering 2025;23(3):215 View
  17. Choi S, Shin J, Kim Y, Shin J, Ko M. Estimating Sleep-Stage Distribution from Respiratory Sounds via Deep Audio Segmentation. Sensors 2025;25(20):6282 View
  18. Wang P, Lin Y, Liu W, Ma E, Majumdar A, Kang J, Gudnason J, Kuan Y, Lee K, Feng P, Chen K, Wu C, Lee H, Chen Y, Chen Y, Chien R, Chang Y, Tsai C. Deep representation learning with cross attention-based multi-feature fusion for sleep apnea detection using sleep respiratory sound. Biomedical Signal Processing and Control 2026;113:109093 View
  19. Jahrami H, Husain W, Trabelsi K, Penzel T, Hirshkowitz M, Razjouyan J, BaHammam A, Sharafkhaneh A. Artificial intelligence and sleep medicine II: A scoping review of applications, advancements, and future directions. Sleep Medicine Reviews 2026;85:102212 View

Conference Proceedings

  1. Tendeas T, Chrisstianto C, Alianto V, Puji M, Permai S, Ningsih R. 2025 9th International Conference on Instrumentation, Control, and Automation (ICA). Embedded System for Early Sleep Apnea Detection Based on Mel-Frequency Cepstral Coefficient (MFCC) and Deep Learning Technique View