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Activity Number:
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157
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304524 |
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Title:
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Nonparametric Spectral Analysis with Applications to Seizure Characterization Using EEG Time Series
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Author(s):
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Li Qin*+ and Yuedong Wang
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Companies:
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Fred Hutchinson Cancer Research Center and University of California, Santa Barbara
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Address:
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1100 Fairview AVE N , Seattle, WA, 98115,
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Keywords:
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GACV, GML ; locally stationary process ; permutation test ; smoothing parameter ; smoothing spline ; smoothing spline ANOVA
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Abstract:
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Understanding the seizure initiation process and its propagation pattern(s) is a critical task in epilepsy research. In this article, we analyze epileptic EEG time series using nonparametric spectral estimation methods to extract information on seizure-specific power and characteristic frequency (or frequency band(s)). Because the EEGs may become non-stationary before seizure events, we develop methods for both stationary and local stationary processes. Based on penalized Whittle likelihood, we propose a direct generalized maximum likelihood (GML) and generalized approximate cross-validation (GACV) methods to estimate smoothing parameters in both smoothing spline spectrum estimation of a stationary time series and smoothing spline ANOVA time-varying spectrum estimation of a locally stationary process.
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