Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification

Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of particular interest and has been extensively...

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Bibliographic Details
Published in:Frontiers in neurology 2020-05, Vol.11, p.375-375
Main Authors: Gao, Yunyuan, Gao, Bo, Chen, Qiang, Liu, Jia, Zhang, Yingchun
Format: Article
Language:English
Subjects:
deep convolutional neural networks
EEG
electroencephalogram
epileptic EEG signal classification
Neurology
power spectrum density energy diagram
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Summary:Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of particular interest and has been extensively investigated. A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper. This methodology first transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically extract features from the PSDED, and finally classifies four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure). It outperforms the existing epilepsy classification methods in terms of accuracy and efficiency. For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data.
ISSN:1664-2295
1664-2295
DOI:10.3389/fneur.2020.00375