[Lisa_seminaires] Reminder - Tomorrow 15:00 - Talk by Mathieu Sinn

Pascal Lamblin lamblinp at iro.umontreal.ca
Jeu 20 Nov 18:49:31 EST 2008


Hi,
Don't forget tomorrow's MITACS-Lisa-McGill seminar!


Title:    Analysis of EEG data by means of ordinal pattern distributions
Speaker:  Mathieu Sinn
Date:     Friday, November 21st, 15:00
Place:    Room 3195 (pav. André-Aisenstadt, UdeM)

Abstract:
Ordinal time series analysis is a new fast and robust approach to the
investigation of long and complex time series, such as
electroencephalographic (EEG) data. The basic idea is to consider
locally the order relations between the values instead of the values
themselves.

An ordinal pattern represents the order relations between a finite
number of equidistant values of a time series. Assuming that these
values are pairwise different, their unique rank order can be simply
described by a permutation. By looking at the distributions of ordinal
patterns (or measures derived from the distributions) in different parts
of a time series, changes in the dynamic of the underlying system can be
detected, such as increasing or decreasing complexity. One derived
measure is the permutation entropy, which is the Shannon entropy of
ordinal pattern distributions. It has been shown that in certain cases
of time series obtained by dynamical systems, the permutation entropy is
related to the Kolmogorov-Sinai entropy which is a well-known measure
for the complexity of time series and the underlying systems,
respectively.

A main application of the permutation entropy so far has been the
detection of epileptic activity in EEG data, as it is widely accepted
that the complexity of the brain dynamics changes (namely, decreases)
during epileptic seizures. However, since clearly different ordinal
pattern distributions may yield very similar permutation entropy values,
we propose to take the whole distributions into account. First attempts
show that a cluster analysis of ordinal pattern distributions may be a
good approach to an automatic separation and classification of EEG data.
Beneath the detection and localization of epileptic activity, another
interesting application of this method is the automatic classification
of sleep stages according to the criterions of Rechtschaffen and Kales.

This is joint work with Karsten Keller.

--
Pascal


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