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


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 12, pp. 587-594, Dec. 2021
https://doi.org/10.3745/KTSDE.2021.10.12.587,   PDF Download:
Keywords: Epileptic Seizure, EEG, CNN, Ensemble model
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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
M. Kim, "Epileptic Seizure Detection Using CNN Ensemble Models Based on Overlapping Segments of EEG Signals," KIPS Transactions on Software and Data Engineering, vol. 10, no. 12, pp. 587-594, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.12.587.

[ACM Style]
Min-Ki Kim. 2021. Epileptic Seizure Detection Using CNN Ensemble Models Based on Overlapping Segments of EEG Signals. KIPS Transactions on Software and Data Engineering, 10, 12, (2021), 587-594. DOI: https://doi.org/10.3745/KTSDE.2021.10.12.587.