An optimal feature set for seizure detection systems for newborn EEG signals

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An optimal feature set for seizure detection systems for newborn EEG signals

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Title: An optimal feature set for seizure detection systems for newborn EEG signals
Author: Zarjam, P; Mesbah, M; Boashash, B
Abstract: A novel automated method is applied to Electroencephalogram (EEG) data to detect seizure events in newborns. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimizing technique based on mutual information feature selection (MIFS) is employed. This technique evaluates a set of candidate features extracted from the WCs to select an informative subset. This subset is used as an input to an artificial neural network (ANN) classifier. The classifier organizes the EEG signal into seizure or non-seizure activities. The training and test sets are obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The optimized results show an average seizure detection rate of 94%.
Description: This paper uses mutual information feature selection and DWT to detect EEG new-born seizures. (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).
URI: http://hdl.handle.net/10576/10729
Date: 2003-05

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