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http://dx.doi.org/10.3745/KTSDE.2021.10.12.587

Epileptic Seizure Detection Using CNN Ensemble Models Based on Overlapping Segments of EEG Signals  

Kim, Min-Ki (경상대학교 컴퓨터과학과, 공학연구원(ERI) 자동화.컴퓨터연구센터)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.12, 2021 , pp. 587-594 More about this Journal
Abstract
As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.
Keywords
Epileptic Seizure; EEG; CNN; Ensemble Model;
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