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

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    Date
    2009-12-10
    Type
    Article
    Author
    Fraiwan, Luay
    Lweesy, Khaldon
    Khasawneh, Natheer
    Fraiwan, Mohammad
    Wenz, Heinrich
    Dickhaus, Hartmut
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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 electroencephalo- gram (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’ record- ings and 0.74 and 0.50 respectively for preterm neonates’ recordings.
    URI
    https://dspace.adu.ac.ae/handle/1/741
    DOI
    https://doi.org/10.1007/s10916-009-9406-2
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    • Electrical Engineering

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