Clustering approach to quantify long-term spatio-temporal interactions in epileptic intracranial electroencephalography

Authors:
Anant Hegde;Deniz Erdogmus;Deng S. Shiau;Jose C. Principe;Chris J. Sackellares
Affiliations:
Computational Neuro Engineering Laboratory, Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL;Department of Computer Science and Electrical Engineering CSEE, OGI School of Science & Engineering, Oregon Health & Science University, Portland, Beaverton, OR;Optima Neuroscience, Inc., Gainesville, FL;Computational Neuro Engineering Laboratory, Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL;Optima Neuroscience, Inc., Gainesville, FL and Malcolm Randal VA Medical Center, Gainesville, FLa, FL
Venue:
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Year:
2007

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Abstract

Abnormal dynamical coupling between brain structures is believed to be primarily responsible for the generation of epileptic seizures and their propagation. In this study, we attempt to identify the spatio-temporal interactions of an epileptic brain using a previously proposed nonlinear dependency measure. Using a clustering model, we determine the average spatial mappings in an epileptic brain at different stages of a complex partial seizure. Results involving 8 seizures from 2 epileptic patients suggest that there may be a fixed pattern associated with regional spatio-temporal dynamics during the interictal to pre-post-ictal transition.