On the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities

Boashash, Boualem and Boubchir, Larbi (2012). On the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities. In: Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. 19th International Conference on Neural Information Processing, ICONIP 2012, Doha, Qatar, (634-643). 12 - 15 November 2012. doi:10.1007/978-3-642-34478-7_77


Author Boashash, Boualem
Boubchir, Larbi
Title of paper On the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities
Conference name 19th International Conference on Neural Information Processing, ICONIP 2012
Conference location Doha, Qatar
Conference dates 12 - 15 November 2012
Proceedings title Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2012
Year available 2012
Sub-type Fully published paper
DOI 10.1007/978-3-642-34478-7_77
Open Access Status
ISBN 9783642344770
9783642344787
ISSN 0302-9743
1611-3349
Volume 7666
Issue 4
Start page 634
End page 643
Total pages 10
Collection year 2013
Language eng
Abstract/Summary This 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.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Keyword Classification
Detection
Features selection
Features translation
Instantaneous frequency
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collection: UQ Centre for Clinical Research Publications
 
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Created: Thu, 28 Nov 2013, 09:24:46 EST by System User on behalf of UQ Centre for Clinical Research