| Item type |
学術雑誌論文 / Journal Article(1) |
| タイトル |
|
|
タイトル |
Analysis of Nonlinear Dynamics in Epilepsy using a Koopman Operator Framework |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Diffusion mapping |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
ECoG |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
epileptic seizures |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
extended dynamic mode decomposition |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Koopman operator |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
nonlinear dynamics |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
time-delay embedding |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
Takahashi, Toshimitsu
Ogino, Masahiro
Uchiyama, Yusuke
Fujiki, Soichiro
Nomoto, Kensaku
Fukushima, Teruyuki
Kawase, Toshihiro
Koganemaru, Satoko
Akutsu, Hiroyoshi
Kansaku, Kenji
|
| 書誌情報 |
en : Dokkyo Medical Journal
巻 4,
号 4,
p. 300-312,
発行日 2025-08-25
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| 記事種別 |
|
|
値 |
Original |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Epilepsy is a disorder characterized by recurrent seizures, and seizure prediction using electroencephalogram (EEG) and electrocorticogram (ECoG) signals is challenging. Conventional signal processing methods, which assume stationarity and linearity for the system considered, are inefficient at capturing the complex temporal structures of these signals; therefore, this study focused on dynamic mode decomposition (DMD), which has recently been used in fluid dynamics to extract the spatiotemporal dynamic features of non-stationary and nonlinear signals. This study applied an advanced data-driven nonlinear time series analysis method that combines time-delay embedding, diffusion mapping, and Koopman operator analysis, to the nonlinear dynamics of epileptic ECoG data. We analyzed 5 min of ECoG data from an 11-year-old boy with refractory Rolandic epilepsy. The collected data were embedded into a high-dimensional time-delayed coordinate space, and then its dimensions were reduced by diffusion mapping. The Koopman operator was estimated using extended DMD (eDMD), yielding its eigenvalues, modes, and eigenfunctions that represent the underlying dynamics of brain activity. We identified 11 of 32 Koopman eigenfunctions as significantly correlated with the occurrence of epileptic spikes, representing the temporal features of the ECoG data. We also found six Koopman modes that were significantly correlated with the spatial pattern of epileptic spike propagation, capturing the spatial features of the ECoG data. Understanding the spatiotemporal brain dynamics underlying epileptic EEG and ECoG signals may provide new clues to seizure prediction in epilepsy. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Dokkyo Medical Society |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2436-522X |
| 書誌レコードID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA12941861 |
| DOI |
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|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.51040/dkmj.2024-049 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
DKMJ-4-4-4.pdf (3.4 MB)
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