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Activity Number: 255
Type: Contributed
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #308727
Title: Modeling Volatility Characteristics of Epileptic EEGs Using GARCH Models
Author(s): Jack Follis*+ and Dejian Lai and Giridhar Kalamangalam
Companies: University of St. Thomas and Univeristy of Texas Health Science Center at Houston School of Public Health and Univeristy of Texas Medical School at Houston
Keywords: EEG ; epilepsy ; seizure ; GARCH
Abstract:

In this talk, the volatility of seizure and non-seizure channels of an epileptic EEG are compared. The volatility half-lives of 66 channels (3 seizure, 63 non-seizure) of an intracranial EEG were determined using ARMA-GARCH models. Volatility half-lives were calculated for one-minute segments and compared using confidence intervals. The volatility half-lives of two of the three seizure channels were found to be significantly lower than the non-seizure channels for segments preceding seizure onset and segments of an awake state. The estimates for the half-lives were also consistent for randomly selected one minute segments. Thus, volatility and GARCH models may be a useful tool in determining hidden properties in epileptic EEGs.


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