DocumentCode :
547737
Title :
Evaluation of some physiological statements about seizure, using processing of epileptic EEG signals
Author :
Shayegh, F. ; Amirfattahi, R. ; Sadri, S. ; Ansari-Asl, K.
Author_Institution :
Digital signal Processing Lab, Department of Electrical and Computer Engineering, Isfahan university of Technology, 84156-83111
fYear :
2011
fDate :
17-19 May 2011
Firstpage :
1
Lastpage :
6
Abstract :
Seizure genesis is usually attributed to imbalance of the excitation and inhibition activities of neurons. It is claimed that slow inhibition mechanism is responsible for controlling the variations of the excitation mechanism, and any deficiency in this function will lead to disturbance of their natural balance. Fast inhibition mechanism is also known to collaborate with slow inhibitory processes. Any proof about the excitation and inhibition mechanisms refers to the ex-vivo or in-vitro experiments, i.e. from the microscopic point of view. Up to now, there is not a straight road into evaluating these hypotheses from the macroscopic view. In this paper we try to interpret the excitation-inhibition sequences extracted from some depth-EEG signals through an identification-based procedure. The aim is to check out whether the statements gained from microscopic observations are re-established in population of neurons in the case of MTLE or not. Using Neyman-Pearson hypothesis test technique we try to answer these questions. We observe that even in population of neurons slow inhibition is responsible for compensating excitatory processes to balance inhibitory /excitatory ratio, such that when it is impaired causes seizures to arise; also the average fast inhibitory process of population of neurons follows its slow inhibition dynamics. Also, we check the differentiation of the excitation-inhibition ratio between normal and ictal states, as well as between normal and pre-ictal states. Ictal activity is properly a state of abnormal high excitation-inhibition ratio but before beginning of seizures there are not unique patterns.
Keywords :
Brain modeling; Cost function; Electroencephalography; Equations; Mathematical model; Neurons; Noise; Neyman-Pearson decision algorithm; Seizure; excitation; inhibition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering (ICEE), 2011 19th Iranian Conference on
Conference_Location :
Tehran, Iran
Print_ISBN :
978-1-4577-0730-8
Electronic_ISBN :
978-964-463-428-4
Type :
conf
Filename :
5955626
Link To Document :
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