Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

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dc.contributor.authorSeo, Jong-Hyeon
dc.contributor.authorTsuda, Ichiro
dc.contributor.authorLee, Young Ju
dc.contributor.authorIkeda, Akio
dc.contributor.authorMatsuhashi, Masao
dc.contributor.authorMatsumoto, Riki
dc.contributor.authorKikuchi, Takayuki
dc.contributor.authorKang, Hunseok
dc.date.accessioned2021-07-28T14:45:10Z
dc.date.available2021-07-28T14:45:10Z
dc.date.issued2020-04-01
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.
dc.description.departmentMathematics
dc.formatText
dc.format.extent18 pages
dc.format.medium1 file (.pdf)
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.
dc.identifier.doihttps://doi.org/10.3390/math8040481
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/10877/14111
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute
dc.rights.holder© 2020 The Authors.
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 International License.
dc.sourceMathematics, 2020, Vol. 8, No. 4, Article 481.
dc.subjectepileptic seizure
dc.subjectdynamic mode decomposition
dc.subjectEEG
dc.subjectECoG
dc.subjectpattern recognition
dc.subjectDC (direct current) shift
dc.subjecthigh-frequency oscillation
dc.subjectMathematics
dc.titlePattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition
dc.typeArticle

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