Enhanced time-frequency features for neonatal EEG seizure detection

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Enhanced time-frequency features for neonatal EEG seizure detection

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dc.contributor.author Hassanpour, H
dc.contributor.author Mesbah, M
dc.contributor.author Boashash, B
dc.date.accessioned 2012-03-12T06:30:06Z
dc.date.available 2012-03-12T06:30:06Z
dc.date.issued 2003-05
dc.identifier.citation Proceedings of the International Symposium on Circuits and Systems, 2003, Issue Date : 25-28 May 2003 Volume : 5 On page(s): V-29 - V-32 vol.5 en_US
dc.identifier.isbn 0-7803-7761-3
dc.identifier.other Digital Object Identifier : 10.1109/ISCAS.2003.1206165
dc.identifier.uri http://hdl.handle.net/10576/10802
dc.description This paper presents an improved time-frequency based technique for detecting seizure activity in the EEG signal of neonates using filtered singular vectors. (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 Time-frequency based methods have been proved to be superior to other methods in analysing neonatal EEG. This is due to the fact that newborn EEG is nonstationary and multicomponent. This paper presents an approach for improving the performance of the EEG seizure detection technique previously introduced by the authors. The proposed approach utilizes the SVD-based technique for both enhancing the time-frequency representation of the signal and extracting EEG seizure features. Enhancing the time-frequency representation leads to improvement in the quality of the extracted feature. To extract the features the estimated distribution functions of the singular vectors associated with the time-frequency representation of the EEG epoch are used to identify the patterns embedded in the signal. The estimated distributed functions related to the seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject EEG seizure detection en_US
dc.subject time-frequency distribution en_US
dc.subject electroencephalogram en_US
dc.subject neonatal EEG en_US
dc.subject seizure detection en_US
dc.subject neural network en_US
dc.subject newborn EEG en_US
dc.subject nonseizure epochs en_US
dc.subject nonseizure patterns en_US
dc.subject seizure epochs en_US
dc.subject seizure patterns en_US
dc.subject singular value decomposition en_US
dc.subject singular vectors en_US
dc.subject time-frequency methods en_US
dc.subject time-frequency features en_US
dc.subject time-frequency representation en_US
dc.title Enhanced time-frequency features for neonatal EEG seizure detection en_US
dc.type Article en_US

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