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AuthorKhlif, M
AuthorColditz, P
AuthorBoashash, B
Available date2014-03-18T16:02:38Z
Publication Date2013-12
Publication NameMedical Engineering & Physics
Identifierhttp://dx.doi.org/10.1016/j.medengphy.2013.07.005
CitationM.S. Khlif, P.B Colditz, B. Boashash, “Effective implementation of time–frequency matched filter with adapted pre and postprocessing for data-dependent detection of newborn seizures”, Medical Engineering & Physics, vol. 35, no. 12, pp. 1762–1769, December 2013.
URIhttp://hdl.handle.net/10576/10960
DescriptionThis paper presents a TF matched filter for detection of neonatal seizure based on cross-correlation betweenthe TFD of EEG signals. (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).
AbstractNeonatal EEG seizures often manifest as nonstationary and multicomponent signals, necessitating analysis in the time–frequency (TF) domain. This paper presents a novel neonatal seizure detector based on effective implementation of the TF matched filter. In the detection process, the TF signatures of EEG seizure are extracted to construct the TF templates used by the matched filter. Matching pursuit (MP) decomposition and narrowband filtering are proposed for the reduction of artifacts prior to seizure detection. Geometrical correlation is used to consolidate the multichannel detections and to reduce the number of false detections due to remnant artifacts. A data-dependent threshold is defined for the classification of EEG. Using 30 newborn EEG records with seizures, the classification process yielded an overall detection accuracy of 92.4% with good detection rate (GDR) of 84.8% and false detection rate of 0.36 FD/h. Better detection performance (accuracy >95%) was recorded for relatively long EEG records with short seizure events.
Languageen
PublisherElsevier
SubjectEEG
Matched filter
Neonatal seizure detection
Time–frequency analysis
Time–frequency distribution
TitleEffective implementation of time–frequency matched filter with adapted pre and postprocessing for data-dependent detection of newborn seizures
TypeArticle
dc.accessType Abstract Only


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