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dc.contributor.authorSeo, Jong-Hyeon ( )
dc.contributor.authorTsuda, Ichiro ( )
dc.contributor.authorLee, Young Ju ( Orcid Icon 0000-0003-3365-9031 )
dc.contributor.authorIkeda, Akio ( Orcid Icon 0000-0002-0790-2598 )
dc.contributor.authorMatsuhashi, Masao ( )
dc.contributor.authorMatsumoto, Riki ( )
dc.contributor.authorKikuchi, Takayuki ( Orcid Icon 0000-0002-6295-5510 )
dc.contributor.authorKang, Hunseok ( )
dc.date.accessioned2021-07-28T14:45:10Z
dc.date.available2021-07-28T14:45:10Z
dc.date.issued2020-04-01
dc.identifier.citationSeo, J. H., Tsuda, I., Lee, Y. J., Ikeda, A., Matsuhashi, M., Matsumoto, R., Kikuchi, T., & Kang, H. (2020). Pattern recognition in epileptic EEG signals via dynamic mode decomposition. Mathematics, 8(4), 481.en_US
dc.identifier.issn2227-7390
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/14111
dc.description.abstractIn this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.en_US
dc.formatText
dc.format.extent18 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.sourceMathematics, 2020, Vol. 8, No. 4, Article 481.
dc.subjectEpileptic seizureen_US
dc.subjectDynamic mode decompositionen_US
dc.subjectEEGen_US
dc.subjectECoGen_US
dc.subjectPattern recognitionen_US
dc.subjectDC (direct current) shiften_US
dc.subjectHigh-frequency oscillationen_US
dc.titlePattern Recognition in Epileptic EEG Signals via Dynamic Mode Decompositionen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.holder© 2020 The Authors.
dc.identifier.doihttps://doi.org/10.3390/math8040481
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.description.departmentMathematics


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