Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/27431
DC FieldValueLanguage
dc.contributor.authorRasekhi, Jalil-
dc.contributor.authorMollaei, Mohammad Reza Karami-
dc.contributor.authorBandarabadi, Mojtaba-
dc.contributor.authorTeixeira, Cesar A.-
dc.contributor.authorDourado, Antonio-
dc.date.accessioned2014-10-29T12:05:47Z-
dc.date.available2014-10-29T12:05:47Z-
dc.date.issued2013-07-30-
dc.identifier.citationRASEKHI, Jalil [et. al] - Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. "Journal of Neuroscience Methods". ISSN 0165-0270. Vol. 217 Nº. 1-2 (2013) p. 9-16por
dc.identifier.issn0165-0270-
dc.identifier.urihttp://hdl.handle.net/10316/27431-
dc.description.abstractCombining multiple linear univariate features in one feature space and classifying the feature space using machine learning methods could predict epileptic seizures in patients suffering from refractory epilepsy. For each patient, a set of twenty-two linear univariate features were extracted from 6 electroencephalogram (EEG) signals to make a 132 dimensional feature space. Preprocessing and normalization methods of the features, which affect the output of the seizure prediction algorithm, were studied in terms of alarm sensitivity and false prediction rate (FPR). The problem of choosing an optimal preictal time was tackled using 4 distinct values of 10, 20, 30, and 40 min. The seizure prediction problem has traditionally been considered a two-class classification problem, which is also exercised here. These studies have been conducted on the features obtained from 10 patients. For each patient, 48 different combinations of methods are compared to find the best configuration. Normalization by dividing by the maximum and smoothing are found to be the best configuration in most of the patients. The results also indicate that applying machine learning methods on a multidimensional feature space of 22 univariate features predicted seizure onsets with high performance. On average, the seizures were predicted in 73.9% of the cases (34 out of 46 in 737.9 h of test data), with a FPR of 0.15 h−1.por
dc.language.isoengpor
dc.publisherElsevierpor
dc.rightsopenAccesspor
dc.subjectSeizure predictionpor
dc.subjectEpilepsypor
dc.subjectClassificationpor
dc.subjectFeatures selectionpor
dc.subjectSpace reductionpor
dc.titlePreprocessing effects of 22 linear univariate features on the performance of seizure prediction methodspor
dc.typearticlepor
degois.publication.firstPage9por
degois.publication.lastPage16por
degois.publication.issue1-2por
degois.publication.titleJournal of Neuroscience Methodspor
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0165027013001246por
dc.peerreviewedYespor
dc.identifier.doi10.1016/j.jneumeth.2013.03.019-
degois.publication.volume217por
item.fulltextCom Texto completo-
item.languageiso639-1en-
item.grantfulltextopen-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0001-9396-1211-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
FCTUC Eng.Informática - Artigos em Revistas Internacionais
Files in This Item:
File Description SizeFormat
Preprocessing effects of 22 linear univariate features.pdf493.55 kBAdobe PDFView/Open
Show simple item record

SCOPUSTM   
Citations

49
checked on May 29, 2020

WEB OF SCIENCETM
Citations 5

52
checked on Jul 2, 2021

Page view(s) 20

561
checked on Jul 21, 2021

Download(s) 20

822
checked on Jul 21, 2021

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.