id: 05668800
dt: a
an: 05668800
au: Mammone, Nadia; Inuso, Giuseppina; La Foresta, Fabio; Versaci, Mario;
    Morabito, Francesco C.
ti: Clustering of entropy topography in epileptic electroencephalography.
so: Palmer-Brown, Dominic (ed.) et al., Engineering applications of neural
    networks. 11th international conference, EANN 2009, London, UK, August
    27‒29, 2009. Proceedings. Berlin: Springer (ISBN
    978-3-642-03968-3/pbk; 978-3-642-03969-0/ebook). Communications in
    Computer and Information Science 43, 453-462 (2009).
py: 2009
pu: Berlin: Springer
la: EN
cc: 
ut: index terms electroencephalography; renyi’s entropy; epilepsy; SOM
ci: 
li: doi:10.1007/978-3-642-03969-0_43
ab: Summary: Epileptic seizures seem to result from an abnormal synchronization
    of different areas of the brain, as if a kind of recruitment occurred
    from a critical area towards other areas of the brain, until the brain
    itself can no longer bear the extent of this recruitment and triggers
    the seizure in order to reset this abnormal condition. In order to
    catch these recruitment phenomena, a technique based on entropy is
    introduced to study the synchronization of the electric activity of
    neuronal sources in the brain and tested over three EEG dataset from
    patients affected by partial epilepsy. Entropy showed a very steady
    spatial distribution and appeared linked to the region of seizure
    onset. Entropy mapping was compared with the standard power mapping
    that was much less stable and selective. A SOM based spatial clustering
    of entropy topography showed that the critical electrodes were coupled
    together long time before the seizure onset.
rv: