Modelling of newborn EEG data for seizure detection

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Modelling of newborn EEG data for seizure detection

Show simple item record Roessgen, M Zoubir, A.M. Boashash, B 2012-03-12T06:42:36Z 2012-03-12T06:42:36Z 1995-07
dc.identifier.citation Proc. SPIE 2563, 101 (1995) en_US
dc.identifier.other Digital Object Identifier
dc.description This paper is one of the earlier papers treating the problem of automatic detection of seizure in the newborn based on the EEG using a model based approach. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see 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: 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 Seizures are often the first sign of neurological disease or dysfunction in the human newborn. Their clinical manifestation however, is often subtle, which tends to hinder their diagnosis. This represents an undesireable situation since the failure to quickly and accurately diagnose seizure can lead to long term brain injury or even death. In this paper, the problem of automatic seizure detection in the newborn based on the electroencephalogram (EEG) is considered. It is shown that good detection performance of electrographic seizure, which is the manifestation of seizure within the EEG, is possible using a new approach which is based on a model for the generation of the EEG. This model is derived from the histology and biophysics of a localized portion of the brain and is thus physically motivated. The model for EEG seizure is first presented along with an estimator for the model parameters. Then a seizure detection scheme based on the model parameter estimates is suggested. The method is then used to detect seizure in both simulated and real newborn EEG data. This approach gives superior performance over conventional classification approaches which rely on training data to produce observable test statistics. This is because, in general, trained classifiers are particularly susceptible to the extreme variability of the EEG over time as well as from patient to patient. en_US
dc.language.iso en en_US
dc.publisher SPIE en_US
dc.subject electroencephalogram en_US
dc.subject EEG modeling en_US
dc.subject newborn EEG en_US
dc.subject estimation, seizure en_US
dc.subject detection en_US
dc.subject classification en_US
dc.title Modelling of newborn EEG data for seizure detection en_US
dc.type Article en_US

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