Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates

  • Authors:

  • Luay Fraiwan;Khaldon Lweesy;Natheer Khasawneh;Mohammad Fraiwan;Heinrich Wenz;Hartmut Dickhaus

  • Affiliations:

  • Biomedical Engineering Department, Jordan University of Science & Technology, Irbid, Jordan;Biomedical Engineering Department, Jordan University of Science & Technology, Irbid, Jordan;Computer Engineering Department, Jordan University of Science & Technology, Irbid, Jordan;Computer Engineering Department, Jordan University of Science & Technology, Irbid, Jordan;Thoracic Clinic, University of Heidelberg, Heidelberg, Germany;Medical informatics Department, University of Heidelberg, Heidelberg, Germany

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2011

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Abstract

This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner---Ville distribution (WVD), Hilbert---Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to represent the EEG signal from which features were extracted using time frequency entropy. The classification of features was done using feed forward back-propagation ANN. The system was trained and tested using data taken from neonates of post-conceptual age of 40 weeks for both preterm (14 recordings) and fullterm (15 recordings). The identification of sleep stages was successfully implemented and the classification based on the WVD outperformed the approaches based on CWT and HHS. The accuracy and kappa coefficient were found to be 0.84 and 0.65 respectively for the fullterm neonates' recordings and 0.74 and 0.50 respectively for preterm neonates' recordings.