BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.2.3.1//EN
TZID:Europe/Paris
X-WR-TIMEZONE:Europe/Paris
BEGIN:VEVENT
UID:5487@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20121213T140000
DTEND;TZID=Europe/Paris:20121213T150000
DTSTAMP:20241028T214945Z
URL:https://www.i2m.univ-amu.fr/evenements/j-marchi-ins-fully-unsupervised
 -detection-and-clustering-of-eeg-epileptic-spikes/
SUMMARY: (...): J. Marchi (INS) : Fully unsupervised detection and clusteri
 ng of EEG epileptic spikes
DESCRIPTION:: Fully unsupervised detection and clustering of EEG epileptic 
 spikes. By Johann Marchi\, INS.\nNowadays\, large amounts of electroenceph
 alogram (EEG) data remain unexploited because of a lack of unsupervised da
 ta mining programs. Indeed\, the visual analysis of EEG signals is a time-
 consuming task for the physician\, and some variability may appear across 
 expert advice\, hence the need of automated and objective algorithms. We w
 ill present a project under progress. This work aims at providing a method
  to detect and classify abrupt changes (epileptic spikes) in interictal EE
 G signals recorded on epileptic patients. The method used for the detectio
 n of spikes is inspired from the local false discovery rate (Efron 2001) w
 hich is based on very few modeling assumptions. Various methods are studie
 d to perform efficient clustering with an adaptive number of classes. To t
 he best of our knowledge\, this is the first fully automated method for th
 e detection and classification of epileptic EEG spikes\, as opposed to mos
 t of the already available algorithms requiring either the intervention of
  an expert to form learning sets or a tedious adjustment of complex parame
 ters.
CATEGORIES:Séminaire,Signal et Apprentissage
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:STANDARD
DTSTART:20121028T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
END:VTIMEZONE
END:VCALENDAR