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    Properties of functional brain networks correlate frequency of psychogenic non-epileptic seizures

    , Article Frontiers in Human Neuroscience ; Issue DEC , 2012 ; 16625161 (ISSN) Barzegaran, E ; Joudaki, A ; Jalili, M ; Rossetti, A. O ; Frackowiak, R. S ; Knyazeva, M. G ; Sharif University of Technology
    Frontiers Media S. A  2012
    Abstract
    Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness metrics, respectively. Yet the number of PNES attacks per month correlated with a... 

    Stockwell transform for epileptic seizure detection from EEG signals

    , Article Biomedical Signal Processing and Control ; Volume 38 , 2017 , Pages 108-118 ; 17468094 (ISSN) Kalbkhani, H ; Shayesteh, M. G ; Sharif University of Technology
    Abstract
    Epilepsy is the most common disorder of human brain. The goal of this paper is to present a new method for classification of epileptic phases based on the sub-bands of electroencephalogram (EEG) signals obtained from the Stockwell transform (ST). ST is a time-frequency analysis that not only covers the advantages of both short-time Fourier transform (FT) and wavelet transform (WT), but also overcomes their shortcomings. In the proposed method, at first, EEG signal is transformed into time-frequency domain using ST and all operations are performed in the new domain. Then, the amplitudes of ST in five sub-bands, namely delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), are computed. In...