Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

Jong-Hyeon Seo, Ichiro Tsuda, Young Ju Lee, Akio Ikeda, Masao Matsuhashi, Riki Matsumoto, Takayuki Kikuchi and Hunseok Kang
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Jong-Hyeon Seo: Chubu University Academy of Emerging Sciences, Chubu University, Kasugai, Aichi 487-8501, Japan
Ichiro Tsuda: Chubu University Academy of Emerging Sciences, Chubu University, Kasugai, Aichi 487-8501, Japan
Young Ju Lee: Department of Mathematics, Texas State University, San Marcos, TX 78666, USA
Akio Ikeda: Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
Masao Matsuhashi: Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
Riki Matsumoto: Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan
Takayuki Kikuchi: Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
Hunseok Kang: Department of Mathematics, College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait

Mathematics, 2020, vol. 8, issue 4, 1-18

Abstract: 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.

Keywords: epileptic seizure; dynamic mode decomposition; EEG; ECoG; pattern recognition; DC (direct current) shift; high-frequency oscillation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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