Comparison of automated deep neural network against manual sleep stage scoring in clinical data.

Journal: Computers in biology and medicine
PMID:

Abstract

OBJECTIVE: To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines.

Authors

  • Hanrong Cheng
    Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, China. Electronic address: cheng.hanrong@szhospital.com.
  • Yifei Yang
    Safety Evaluation Center for Chinese Materia Medica, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
  • Jingshu Shi
    Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.
  • Zhangbo Li
    Shenzhen Gianta Information Technology Co., LTD, Shenzhen, 518048, China.
  • Yang Feng
    Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • Xingjun Wang
    Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. wangxingjun@tsinghua.edu.cn.