Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition
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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.
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.
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