• DocumentCode
    3495779
  • Title

    Analysis of absence seizure EEG via Permutation Entropy spatio-temporal clustering

  • Author

    Mammone, Nadia ; Morabito, Francesco C.

  • Author_Institution
    Dept. DIMET, Mediterranean Univ. of Reggio Calabria, Reggio Calabria, Italy
  • fYear
    2011
  • fDate
    July 31 2011-Aug. 5 2011
  • Firstpage
    1417
  • Lastpage
    1422
  • Abstract
    The genesis of epileptic seizures is nowadays still mostly unknown. The hypothesis that most of scientist share is that an abnormal synchronization of different groups of neurons seems to trigger a recruitment mechanism that leads the brain to the seizure in order to reset this abnormal condition. If this is the case, a gradual transformation of the characteristics of the EEG can be hypothesized. It is therefore necessary to find a parameter that is able to measure the synchronization level in the EEG and, since the spatial dimension has to be taken into account if we aim to find out how the different areas in the brain recruit each other to develop the seizure, a spatio-temporal analysis of this parameter has to be carried out. In the present paper, a spatio-temporal analysis of EEG synchronization in 24 patients affected by absence seizure is proposed and the results are hereby reported and compared to the results obtained with a group of 40 healthy subjects. The spatio-temporal analysis is based on Permutation Entropy (PE). We found out that, ever since the interictal stages, fronto-temporal areas appear constantly associated to PE levels that are higher compared to the rest of the brain, whereas the parietal/occipital areas appear associated to low-PE. The brain of healthy subjects seems to behave in a different way because we could not see a recurrent behaviour of PE topography.
  • Keywords
    electroencephalography; medical signal processing; pattern clustering; EEG; PE; abnormal synchronization; epileptic seizures; permutation entropy spatiotemporal clustering; recruitment mechanism; spatial dimension; spatiotemporal analysis; Clustering algorithms; Complexity theory; Electrodes; Electroencephalography; Entropy; Synchronization; Time series analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2011 International Joint Conference on
  • Conference_Location
    San Jose, CA
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4244-9635-8
  • Type

    conf

  • DOI
    10.1109/IJCNN.2011.6033390
  • Filename
    6033390