Next Article in Journal
A Study on Cubic H-Relations in a Topological Universe Viewpoint
Previous Article in Journal
δ(2,2)-Invariant for Lagrangian Submanifolds in Quaternionic Space Forms
Open AccessArticle

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

1
Chubu University Academy of Emerging Sciences, Chubu University, Kasugai, Aichi 487-8501, Japan
2
Department of Mathematics, Texas State University, San Marcos, TX 78666, USA
3
Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
4
Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
5
Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
6
Department of Mathematics, College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(4), 481; https://doi.org/10.3390/math8040481
Received: 22 January 2020 / Revised: 22 February 2020 / Accepted: 24 February 2020 / Published: 1 April 2020
(This article belongs to the Section Dynamical Systems)
In 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. View Full-Text
Keywords: epileptic seizure; dynamic mode decomposition; EEG; ECoG; pattern recognition; DC (direct current) shift; high-frequency oscillation epileptic seizure; dynamic mode decomposition; EEG; ECoG; pattern recognition; DC (direct current) shift; high-frequency oscillation
Show Figures

Figure 1

MDPI and ACS Style

Seo, J.-H.; Tsuda, I.; Lee, Y.J.; Ikeda, A.; Matsuhashi, M.; Matsumoto, R.; Kikuchi, T.; Kang, H. Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition. Mathematics 2020, 8, 481.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop