Skip to main content

01.08.2011 | Original Paper | Ausgabe 4/2011

Journal of Medical Systems 4/2011

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

Journal of Medical Systems > Ausgabe 4/2011
Luay Fraiwan, Khaldon Lweesy, Natheer Khasawneh, Mohammad Fraiwan, Heinrich Wenz, Hartmut Dickhaus


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.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

e.Med Interdisziplinär

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf

Über diesen Artikel

Weitere Artikel der Ausgabe 4/2011

Journal of Medical Systems 4/2011 Zur Ausgabe