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dc.contributor.authorShen, Chia-Pingen_US
dc.contributor.authorChen, Chih-Chuanen_US
dc.contributor.authorHsieh, Sheau -Lingen_US
dc.contributor.authorChen, Wei-Hsinen_US
dc.contributor.authorChen, Jia-Mingen_US
dc.contributor.authorChen, Chih-Minen_US
dc.contributor.authorLai, Feipeien_US
dc.contributor.authorChiu, Ming-Jangen_US
dc.date.accessioned2014-12-08T15:33:43Z-
dc.date.available2014-12-08T15:33:43Z-
dc.date.issued2013-10-01en_US
dc.identifier.issn1550-0594en_US
dc.identifier.urihttp://dx.doi.org/10.1177/1550059413483451en_US
dc.identifier.urihttp://hdl.handle.net/11536/23307-
dc.description.abstractThe classification of electroencephalography (EEG) signals is one of the most important methods for seizure detection. However, verification of an atypical epileptic seizure often can only be done through long-term EEG monitoring for 24 hours or longer. Hence, automatic EEG signal analysis for clinical screening is necessary for the diagnosis of epilepsy. We propose an EEG analysis system of seizure detection, based on a cascade of wavelet-approximate entropy for feature selection, Fisher scores for adaptive feature selection, and support vector machine for feature classification. Performance of the system was tested on open source data, and the overall accuracy reached 99.97%. We further tested the performance of the system on clinical EEG obtained from a clinical EEG laboratory and bedside EEG recordings. The results showed an overall accuracy of 98.73% for routine EEG, and 94.32% for bedside EEG, which verified the high performance and usefulness of such a cascade system for seizure detection. Also, the prediction model, trained by routine EEG, can be successfully generalized to bedside EEG of independent patients.en_US
dc.language.isoen_USen_US
dc.subjectapproximate entropyen_US
dc.subjectepilepsyen_US
dc.subjectsupport vector machineen_US
dc.subjectelectroencephalogramen_US
dc.titleHigh-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With Clinical Validationen_US
dc.typeArticleen_US
dc.identifier.doi10.1177/1550059413483451en_US
dc.identifier.journalCLINICAL EEG AND NEUROSCIENCEen_US
dc.citation.volume44en_US
dc.citation.issue4en_US
dc.citation.spage247en_US
dc.citation.epage256en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000328275400001-
dc.citation.woscount2-
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