Parameter pattern discovery in nonlinear dynamic model for EEGs analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sun-Hee | - |
dc.contributor.author | Faloutsos, Christos | - |
dc.contributor.author | Yang, Hyung-Jeong | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.date.accessioned | 2021-09-03T20:49:23Z | - |
dc.date.available | 2021-09-03T20:49:23Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-09 | - |
dc.identifier.issn | 0219-6352 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/87728 | - |
dc.description.abstract | We propose a nonlinear dynamic model for an invasive electroencephalogram analysis that learns the optimal parameters of the neural population model via the Levenberg-Marquardt algorithm. We introduce the crucial windows where the estimated parameters present patterns before seizure onset. The optimal parameters minimizes the error between the observed signal and the generated signal by the model. The proposed approach effectively discriminates between healthy signals and epileptic seizure signals. We evaluate the proposed method using an electroencephalogram dataset with normal and epileptic seizure sequences. The empirical results show that the patterns of parameters as a seizure approach and the method is efficient in analyzing nonlinear epilepsy electroencephalogram data. The accuracy of estimating the optimal parameters is improved by using the nonlinear dynamic model. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IMR PRESS | - |
dc.subject | CORRELATION DIMENSION | - |
dc.subject | PREDICTION | - |
dc.subject | PREDICTABILITY | - |
dc.subject | SIGNALS | - |
dc.title | Parameter pattern discovery in nonlinear dynamic model for EEGs analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sun-Hee | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.identifier.doi | 10.1142/S0219635216500242 | - |
dc.identifier.wosid | 000388756700008 | - |
dc.identifier.bibliographicCitation | JOURNAL OF INTEGRATIVE NEUROSCIENCE, v.15, no.3, pp.381 - 402 | - |
dc.relation.isPartOf | JOURNAL OF INTEGRATIVE NEUROSCIENCE | - |
dc.citation.title | JOURNAL OF INTEGRATIVE NEUROSCIENCE | - |
dc.citation.volume | 15 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 381 | - |
dc.citation.endPage | 402 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.subject.keywordPlus | CORRELATION DIMENSION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | PREDICTABILITY | - |
dc.subject.keywordPlus | SIGNALS | - |
dc.subject.keywordAuthor | Epileptic seizure | - |
dc.subject.keywordAuthor | nonlinear dynamic model | - |
dc.subject.keywordAuthor | neurons population | - |
dc.subject.keywordAuthor | electroencephalogram | - |
dc.subject.keywordAuthor | parameter changes | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea 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.