Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.

Journal: BMC anesthesiology
Published Date: Mar 2, 2021

Abstract

BACKGROUND: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment.

Authors

  • Jian Zhan
    School of Information and Communication Technology and Institue for Glycomics, Griffith University, Parklands Drive, Southport, Queensland, 4215, Australia.
  • Zhuo-Xi Wu
    Department of Anaesthesiology, The Second Affiliated Hospital of Army Medical University, Chongqing, 400037, China.
  • Zhen-Xin Duan
    Department of Anaesthesiology, The Second Affiliated Hospital of Army Medical University, Chongqing, 400037, China.
  • Gui-Ying Yang
    Department of Anaesthesiology, The Second Affiliated Hospital of Army Medical University, Chongqing, 400037, China.
  • Zhi-Yong Du
    Department of Anaesthesiology, The Second Affiliated Hospital of Army Medical University, Chongqing, 400037, China.
  • Xiao-Hang Bao
    Department of Anaesthesiology, The Second Affiliated Hospital of Army Medical University, Chongqing, 400037, China.
  • Hong Li
    Department of Public Health Sciences, Medical College of South Carolina, Charleston, SC.

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