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Author Stevenson, N.Jen_US
Author Mesbah, Men_US
Author Boylan, G.Ben_US
Author Colditz, P.Ben_US
Author Boashash, Ben_US
Available date 2012-01-15T08:42:56Zen_US
Publication Date 2010-09en_US
Publication Name Annals of Biomedical Engineering
Citation Stevenson, N. J., Mesbah, M., Boylan, G. B., Colditz, P. B., & Boashash, B. (2010). A Nonlinear Model of Newborn EEG with Nonstationary Inputs. Annals of Biomedical Engineering, 38(9), 3010–3021en_US
URI http://hdl.handle.net/10576/10780en_US
URI http://dx.doi.org/10.1007/s10439-010-0041-3
Description This paper proposes a new model of newborn EEG to simulate different components of newborn EEG, based on the interpretation of the seizure as a sequence of internally evoked potentials. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see http://www.elsevier.com/locate/isbn/0080443354). In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: www.time-frequency.net. This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).en_US
Abstract Newborn EEG is a complex multiple channel signal that displays nonstationary and nonlinear characteristics. Recent studies have focussed on characterizing the manifestation of seizure on the EEG for the purpose of automated seizure detection. This paper describes a novel model of newborn EEG that can be used to improve seizure detection algorithms. The new model is based on a nonlinear dynamic system; the Duffing oscillator. The Duffing oscillator is driven by a nonstationary impulse train to simulate newborn EEG seizure and white Gaussian noise to simulate newborn EEG background. The use of a nonlinear dynamic system reduces the number of parameters required in the model and produces more realistic, life-like EEG compared with existing models. This model was shown to account for 54% of the linear variation in the time domain, for seizure, and 85% of the linear variation in the frequency domain, for background. This constitutes an improvement in combined performance of 6%, with a reduction from 48 to 4 model parameters, compared to an optimized implementation of the best performing existing model.en_US
Language enen_US
Publisher Springer USen_US
Subject time-frequency modelingen_US
Subject instantaneous frequencyen_US
Subject Newbornen_US
Subject Neonateen_US
Subject EEGen_US
Subject Modeling and simulationen_US
Subject Nonlinearen_US
Subject Duffing oscillatoren_US
Subject Nonstationaryen_US
Subject Seizureen_US
Title A Nonlinear Model of Newborn EEG with Nonstationary Inputsen_US
Type Articleen_US


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