Abstract Details
Activity Number:
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255
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #308727 |
Title:
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Modeling Volatility Characteristics of Epileptic EEGs Using GARCH Models
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Author(s):
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Jack Follis*+ and Dejian Lai and Giridhar Kalamangalam
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Companies:
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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
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Keywords:
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EEG ;
epilepsy ;
seizure ;
GARCH
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Abstract:
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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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Authors who are presenting talks have a * after their name.
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