A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals.

Journal: Journal of neural engineering
Published Date:

Abstract

OBJECTIVE: Currently, the automatic sleep staging methods mainly face two problems: the first problem is that although the algorithms which use electroencephalogram (EEG) signals perform well, acquiring EEG signals is complicated and uncomfortable; the second problem is that if the methods utilize physiological signals collected by user-friendly devices, such as cardiorespiratory signals, whose accuracies are hard to be accepted by clinicians, although the employed signals are easy and comfortable to acquire.

Authors

  • Chenglu Sun
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Jiahao Fan
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Yuanting Zhang
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.