Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device.

Journal: Computers in biology and medicine
Published Date: Dec 1, 2018

Abstract

BACKGROUND: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wrist actigraphy derived from a wearable device.

Authors

  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Weixuan Kou
    School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China. Electronic address: weixuankou@outlook.com.
  • Eric I-Chao Chang
    Microsoft Research Asia, Beijing, China. eric.chang@microsoft.com.
  • He Gao
    Clinical Sleep Medicine Center, The General Hospital of the Air Force, Beijing, 100142, China. Electronic address: bjgaohe@sohu.com.
  • Yubo Fan
    State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. yubofan@buaa.edu.cn.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.

Keywords

External Resources

Popular Topics
Recent Journals