A. Acar (USA), C.A. Bingöl, H. Bingöl (Turkey), and B. Yener (USA)
biomedical computing, data mining, unsupervised learn ing, multiway analysis, epileptic focus
Epilepsy surgergy outcome strongly depends on the local ization of epileptic focus. The analysis of ictal EEG (scalp or intracranial) is a gold standard for definition of localiza tion of epileptic focus. In order to automate visual analysis of large amounts of EEG data, we examine the correlations among electrodes captured by linear, nonlinear and multi linear data analysis techniques. We study the performance of these statistical tools to understand the complex structure of epilepsy seizure and localize seizure origin. Our analysis results on four patients with temporal lobe epilepsy reveal that multiway (Tucker3 [1]) and nonlinear multiway (Ker nelized Tucker3) analysis techniques are capable of captur ing epileptic focus precisely when validated with clinical findings whereas linear and nonlinear analysis techniques (SVD, Kernel PCA [2]) fail to localize seizure origin.
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