On predicting epileptic seizures from intracranial electroencephalography
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, Yongseok | - |
dc.date.available | 2021-03-17T08:45:27Z | - |
dc.date.created | 2021-02-26 | - |
dc.date.issued | 2017-02 | - |
dc.identifier.issn | 2093-9868 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/13177 | - |
dc.description.abstract | This study investigates the sensitivity and specificity of predicting epileptic seizures from intracranial electroencephalography (iEEG). A monitoring system is studied to generate an alarm upon detecting a precursor of an epileptic seizure. The iEEG traces of ten patients suffering from medically intractable epilepsy were used to build a prediction model. From the iEEG recording of each patient, power spectral densities were calculated and classified using support vector machines. The prediction results varied across patients. For seven patients, seizures were predicted with 100% sensitivity without any false alarms. One patient showed good sensitivity but lower specificity, and the other two patients showed lower sensitivity and specificity. Predictive analytics based on the spectral feature of iEEG performs well for some patients but not all. This result highlights the need for patient-specific prediction models and algorithms. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGERNATURE | - |
dc.subject | SPECTRAL POWER | - |
dc.subject | EEG | - |
dc.title | On predicting epileptic seizures from intracranial electroencephalography | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoo, Yongseok | - |
dc.identifier.doi | 10.1007/s13534-017-0008-5 | - |
dc.identifier.scopusid | 2-s2.0-85014716236 | - |
dc.identifier.wosid | 000446431300001 | - |
dc.identifier.bibliographicCitation | BIOMEDICAL ENGINEERING LETTERS, v.7, no.1, pp.1 - 5 | - |
dc.relation.isPartOf | BIOMEDICAL ENGINEERING LETTERS | - |
dc.citation.title | BIOMEDICAL ENGINEERING LETTERS | - |
dc.citation.volume | 7 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 5 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.subject.keywordPlus | SPECTRAL POWER | - |
dc.subject.keywordPlus | EEG | - |
dc.subject.keywordAuthor | Predictive analytics | - |
dc.subject.keywordAuthor | Seizure prediction | - |
dc.subject.keywordAuthor | EEG | - |
dc.subject.keywordAuthor | Cross-validation | - |
dc.subject.keywordAuthor | Personalized healthcare | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.