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

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