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AuthorBoashash B.
AuthorBoubchir L.
Available date2022-05-31T19:01:38Z
Publication Date2012
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-642-34478-7_77
URIhttp://hdl.handle.net/10576/31926
AbstractThis paper presents new time-frequency features for seizure detection in newborn EEG signals. These features are obtained by translating some relevant time features or frequency features to the joint time-frequency domain. A calibration procedure is then used for verification. The relevant translated features are ranked and selected according to maximal-relevance and minimal-redundancy criteria. The selected features improve the performance of newborn EEG seizure detection and classification systems by up to 4% for 100 real newborn EEG segments.
Languageen
SubjectFeatures selection
Instantaneous frequency
seizure
Time frequency analysis
Time frequency features
Classification (of information)
Data processing
Error detection
Signal detection
Feature extraction
TitleOn the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities
TypeConference Paper
Pagination634-643
Issue NumberPART 4
Volume Number7666 LNCS
dc.accessType Abstract Only


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