IRIS publication 58462106
Performance assessment for EEG-based neonatal seizure detectors.
RIS format for Endnote and similar
TY - JOUR - Temko, A; Thomas, E; Marnane, W; Lightbody, G; Boylan, G - 2011 - March - Clinical Neurophysiology - Performance assessment for EEG-based neonatal seizure detectors. - Published - WOS: 79 () - 122 - 3 - 474 - 482 - This study discusses an appropriate framework to measure system performance for the task of neonatal seizure detection using EEG. The framework is used to present an extended overview of a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. - 10.1016/j.clinph.2010.06.035 DA - 2011/03 ER -
BIBTeX format for JabRef and similar
@article{V58462106, = {Temko, A and Thomas, E and Marnane, W and Lightbody, G and Boylan, G}, = {2011}, = {March}, = {Clinical Neurophysiology}, = {Performance assessment for EEG-based neonatal seizure detectors.}, = {Published}, = {WOS: 79 ()}, = {122}, = {3}, pages = {474--482}, = {{This study discusses an appropriate framework to measure system performance for the task of neonatal seizure detection using EEG. The framework is used to present an extended overview of a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier.}}, = {10.1016/j.clinph.2010.06.035}, source = {IRIS} }
Data as stored in IRIS
AUTHORS | Temko, A; Thomas, E; Marnane, W; Lightbody, G; Boylan, G | ||
YEAR | 2011 | ||
MONTH | March | ||
JOURNAL_CODE | Clinical Neurophysiology | ||
TITLE | Performance assessment for EEG-based neonatal seizure detectors. | ||
STATUS | Published | ||
TIMES_CITED | WOS: 79 () | ||
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VOLUME | 122 | ||
ISSUE | 3 | ||
START_PAGE | 474 | ||
END_PAGE | 482 | ||
ABSTRACT | This study discusses an appropriate framework to measure system performance for the task of neonatal seizure detection using EEG. The framework is used to present an extended overview of a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. | ||
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DOI_LINK | 10.1016/j.clinph.2010.06.035 | ||
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