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A Study on the Epileptic Seizure Prediction using CNN  

Ryu, Sanguk (Department of Computer Software, Hanyang University)
Lee, Namhwa (Department of Computer Software, Hanyang University)
Lee, Yeonsu (Department of Computer Software, Hanyang University)
Joe, Inwhee (Department of Computer Software, Hanyang University)
Min, Kyeongyuk (Department of Electronic Engineering, Hanyang University)
Kim, Taeksoo (EDA Elitech Co. Ltd.)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.2, 2020 , pp. 92-95 More about this Journal
Abstract
In this paper, the new architecture of seizure prediction using CNN and LSTM and DWT was presented. In the proposed architecture, EEG data was labeled into a preictal and interictal section, and DWT was adopted to the preprocessing process to apply the characteristics of the time and frequency domain of the processed EEG signal. Also, CNN was applied to extract the spatial characteristics of each electrode used for EEG measurement, and LSTM neural network was applied to verify the logical order of the preictal section. The learning of the proposed architecture utilizes the CHB-MIT Scalp EEG dataset, and the sliding window technique is applied to balance the dataset between the number of interictal sections and the number of preictal sections. As a result of the simulation of the proposed architecture, a sensitivity of 81.22% and an FPR of 0.174 were obtained.
Keywords
EEG; Epilepsy EEG; Interictal; Preictal; Seizure; CNN; LSTM;
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Times Cited By KSCI : 6  (Citation Analysis)
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