Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

Mathematics

Ichiro Tsuda¹  Jong-Hyeon Seo¹  Masao Matsuhashi²  Akio Ikeda²  Hunseok Kang³  YoungJu Lee⁴  Takayuki Kikuchi⁵  Riki Matsumoto⁶ 

[1] Chubu University Academy of Emerging Sciences, Chubu University, Kasugai, Aichi 487-8501, Japan;Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan;Department of Mathematics, College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait;Department of Mathematics, Texas State University, San Marcos, TX 78666, USA;Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan;Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan;

关键词: epileptic seizure;    dynamic mode decomposition;    EEG;    ECoG;    pattern recognition;    DC (direct current) shift;   

DOI  :  10.3390/math8040481

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown