A Nonlinear Model of Newborn EEG with Nonstationary Inputs

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A Nonlinear Model of Newborn EEG with Nonstationary Inputs

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dc.contributor.advisor
dc.contributor.author Stevenson, N.J
dc.contributor.author Mesbah, M
dc.contributor.author Boylan, G.B
dc.contributor.author Colditz, P.B
dc.contributor.author Boashash, B
dc.date.accessioned 2012-01-15T08:42:56Z
dc.date.available 2012-01-15T08:42:56Z
dc.date.issued 2010-09
dc.identifier.citation ANNALS OF BIOMEDICAL ENGINEERING, Volume 38, Number 9, 3010-302 en_US
dc.identifier.other DOI: 10.1007/s10439-010-0041-3
dc.identifier.uri http://hdl.handle.net/10576/10780
dc.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
dc.description.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
dc.language.iso en en_US
dc.publisher SpringerLink en_US
dc.subject time-frequency modeling en_US
dc.subject instantaneous frequency en_US
dc.subject Newborn
dc.subject Neonate
dc.subject EEG
dc.subject Modeling and simulation
dc.subject Nonlinear
dc.subject Duffing oscillator
dc.subject Nonstationary
dc.subject Seizure
dc.title A Nonlinear Model of Newborn EEG with Nonstationary Inputs en_US
dc.type Article en_US

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