|Title:||Localizing the focus of epileptic seizures using modern measures from multivariate time series analysis||Language:||English||Authors:||Schuster, Thomas
|Qualification level:||Diploma||Keywords:||epileptische Anfälle; ECoG-Daten; partielle Kohärenz; partial directed coherence; verallgemeinerte partial directed coherence; Granger-Kausalität; rekursive kleinste Quadrate
epileptic seizures; ECoG data; partial coherence; partial directed coherence; generalized partial directed coherence; Granger causality; recursive least squares
|Advisor:||Deistler, Manfred||Issue Date:||2009||Number of Pages:||148||Qualification level:||Diploma||Abstract:||
In this diploma thesis, we present different methods to realize the visualization of the spatio-temporal evolution of an epileptic seizure's focus based on multichannel ECoG data. Furthermore, our methods allow for a precise localization of the seizure's initial focus, which is possible, as that focus exists because all our data stems from patients suffering from temporal lobe epilepsy.
Two main methods are presented, starting with a frequency domain approach. Based on the coefficients of a fitted AR-model, modern measures like partial directed coherence (PDC) are derived and discussed. Exhaustive analysis of PDC's problems further leads to a generalized and far better performing version of PDC. In the second part, a Recursive Least Squares (RLS) Algorithm is performed instead of the ordinary least squares approach in frequency domain. This RLS-Algorithm helps us to cope with the instationarities of the ECoG signals far better.
Based on the time-dependent AR-model, Granger causality is used to indicate interactions between channels of the multivariate signals.
Starting with Granger's basic idea and the analysis of coupling effects between two signals, stepwise partialization leads to improved results.
Generalized PDC as well as the Granger causality finally lead to a visualization of the seizure's evolution. The results presented are in excellent accordance with descriptions from clinical experts.
|Library ID:||AC07452684||Organisation:||E105 - Institut für Wirtschaftsmathematik||Publication Type:||Thesis
|Appears in Collections:||Thesis|
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checked on Feb 18, 2021
checked on Feb 18, 2021
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