Detection of newborn EEG seizure using optimal features based on discrete wavelet transform

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Detection of newborn EEG seizure using optimal features based on discrete wavelet transform

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dc.contributor.author Zarjam, P
dc.contributor.author Mesbah, M
dc.contributor.author Boashash, B
dc.date.accessioned 2012-02-15T05:22:58Z
dc.date.available 2012-02-15T05:22:58Z
dc.date.issued 2003
dc.identifier.citation Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on Issue Date : 6-10 April 2003 Volume : 2, On page(s): II - 265-8 vol.2 en_US
dc.identifier.issn 1520-6149
dc.identifier.uri http://hdl.handle.net/10576/10781
dc.description This paper proposes the use of the DWT and ANN for neonatal EEG seizure detection. (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 A new automated method is proposed to detect seizure events in newborns from electroencephalogram (EEG) data. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimal feature subset is obtained using the mutual information evaluation function (MIEF). The MIEF algorithm evaluates a set of candidate features extracted from WCs to select an informative feature subset. The subset is then fed to an artificial neural network (ANN) classifier that organizes the EEG signal into seizure or non-seizure activity. The performance of the proposed features is compared with that of the features obtained using a mutual information feature selection (MIFS) algorithm. The training and test sets are obtained from EEG data acquired from 5 neonates with ages ranging from 2 days to 2 weeks. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject ANN classifier en_US
dc.subject artificial neural network classifier en_US
dc.subject discrete wavelet transform en_US
dc.subject electroencephalogram en_US
dc.subject feature extraction en_US
dc.subject mutual information evaluation function en_US
dc.subject mutual information feature selection en_US
dc.subject newborn EEG seizure detection en_US
dc.subject seizure events en_US
dc.subject wavelet coefficients en_US
dc.subject time-frequency analysis en_US
dc.title Detection of newborn EEG seizure using optimal features based on discrete wavelet transform en_US
dc.type Article en_US

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