Efficient sleep classification based on entropy features and a support vector machine classifier.

Journal: Physiological measurement
Published Date: Nov 26, 2018

Abstract

OBJECTIVE: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method based on entropy features and a support vector machine classifier, named SC-En&SVM.

Authors

  • Zhimin Zhang
    School of Control Science and Engineering, Shandong University, Jinan, People's Republic of China. School of Information Technology and Electrical Engineering, University of Queensland, Queensland, Australia.
  • Shoushui Wei
    School of Control Science and Engineering, Shandong University, Jinan 250061, China.
  • Guohun Zhu
  • Feifei Liu
  • Yuwen Li
  • Xiaotong Dong
  • Chengyu Liu
    Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Feng Liu
    Department of Vascular and Endovascular Surgery, The First Medical Center of Chinese PLA General Hospital, 100853 Beijing, China.

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