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

Artificial Intelligence (1752) New Methods (1031) Insomnia and Sleep Hygiene (209) mHealth for Data Collection and Research (1086) Mobile Health (mhealth) (3164) Wearable Devices and Sensors (1001) Sleep Monitoring, Sleep Quality, Sleep Disorders (229) Machine Learning (1747)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46216, first published .
Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation

Authors of this article:

Hai Hong Tran1 Author Orcid Image ;   Jung Kyung Hong1, 2 Author Orcid Image ;   Hyeryung Jang3 Author Orcid Image ;   Jinhwan Jung1 Author Orcid Image ;   Jongmok Kim1 Author Orcid Image ;   Joonki Hong1 Author Orcid Image ;   Minji Lee4 Author Orcid Image ;   Jeong-Whun Kim5, 6 Author Orcid Image ;   Clete A Kushida7 Author Orcid Image ;   Dongheon Lee1 Author Orcid Image ;   Daewoo Kim1 Author Orcid Image ;   In-Young Yoon2, 4 Author Orcid Image
Article Authors Cited by (9) Tweetations (7) Metrics

Journals

  1. Lee T, Cho Y, Cha K, Jung J, Cho J, Kim H, Kim D, Hong J, Lee D, Keum M, Kushida C, Yoon I, Kim J. Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study. JMIR mHealth and uHealth 2023;11:e50983 View
  2. Cho C. Revolutionizing Sleep Health: The Promise and Challenges of Digital Phenotyping. Chronobiology in Medicine 2023;5(3):95 View
  3. Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Medicine Reviews 2024;74:101897 View
  4. Liu P, Qian W, Zhang H, Zhu Y, Hong Q, Li Q, Yao Y. Automatic sleep stage classification using deep learning: signals, data representation, and neural networks. Artificial Intelligence Review 2024;57(11) View
  5. Mulo J, Liang H, Qian M, Biswas M, Rawal B, Guo Y, Yu W. Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things. Future Internet 2025;17(3):107 View
  6. Liu Q, Wei J, Penzel T, De Vos M, Zhang Y, Huang Z, Poluektov M, Zhu Y, Li C. ActiveSleepLearner: Less Annotation Budget for Better Large-Scale Sleep Staging. IEEE Transactions on Emerging Topics in Computational Intelligence 2025;9(2):1756 View
  7. Xie J, Fonseca P, van Dijk J, Overeem S, Long X. Multi-modal multi-task deep neural networks for sleep disordered breathing assessment using cardiac and audio signals. International Journal of Medical Informatics 2025;201:105932 View
  8. Kim J, Kim S, Cho E, Kyung H, Park S, Hong J, Lee D, Oh J, Yoon I. Evaluation of sound-based sleep stage prediction in shared sleeping settings. Sleep Medicine 2025;132:106533 View

Conference Proceedings

  1. Singh R, Katarya R. 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC). Recent Trends in Human Breathing Detection Using Radar, WiFi and Acoustics View