A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network.

Journal: European neurology
Published Date: Oct 29, 2020

Abstract

INTRODUCTION: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN).

Authors

  • Foad Moradi
    Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Hiwa Mohammadi
    Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran, hiwa.mohamadi@gmail.com.
  • Mohammad Rezaei
    Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Payam Sariaslani
    Department of Neurology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Nazanin Razazian
    Department of Neurology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Habibolah Khazaie
    Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Hojjat Adeli
    Departments of Biomedical Engineering, Biomedical Informatics, Neurology, Neuroscience, Electrical and Computer Engineering, Civil, Environmental, and Geodetic Engineering, and Biophysics Graduate Program, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43210, USA. adeli.1@osu.edu.

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