Neonatal EEG sleep stage classification based on deep learning and HMM.

Journal: Journal of neural engineering
Published Date: Jun 25, 2020

Abstract

OBJECTIVE: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates.

Authors

  • Hojat Ghimatgar
    Department of Electrical and Electronic Engineering, Shiraz University of Technology, P. O. Box 71555-313, Shiraz, Iran.
  • Kamran Kazemi
    Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.
  • Mohammad Sadegh Helfroush
    Department of Electrical and Electronic Engineering, Shiraz University of Technology, P. O. Box 71555-313, Shiraz, Iran.
  • Kirubin Pillay
  • Anneleen Dereymaker
  • Katrien Jansen
  • Maarten De Vos
  • Ardalan Aarabi
    Laboratory of Functional Neuroscience and Pathologies (LNFP, EA4559), University Research Center (CURS), CHU AMIENS - SITE SUD, Avenue Laënnec, Salouël 80420, France; Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France. Electronic address: ardalan.aarabi@u-picardie.fr.

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