A hybrid self-attention deep learning framework for multivariate sleep stage classification.

Journal: BMC bioinformatics
Published Date: Dec 2, 2019

Abstract

BACKGROUND: Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-related disorders. To alleviate the time consumption caused by manual visual inspection of PSG, automatic multivariate sleep stage classification has become an important research topic in medical and bioinformatics.

Authors

  • Ye Yuan
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Kebin Jia
    College of Information and Communication Engineering, Beijing University of Technology, Beijing, China. kebinj@bjut.edu.cn.
  • Fenglong Ma
    Department of Computer Science and Engineering, University at Buffalo, NY, USA.
  • Guangxu Xun
    Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, USA. guangxux@buffalo.edu.
  • Yaqing Wang
    University at Buffalo, Buffalo, NY, USA.
  • Lu Su
    Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA.
  • Aidong Zhang
    Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, USA.

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