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

Frontiers in Neurology
Yunyuan GaoYingchun Zhang

Abstract

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.

References

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Citations

Dec 15, 2020·Scientific Reports·Catalina GómezMario Valderrama
Apr 6, 2021·PeerJ. Computer Science·Shiu KumarAlok Sharma
Sep 9, 2020·Journal of Neuroscience Methods·Yunyuan GaoYingchun Zhang
Apr 27, 2021·Frontiers in Computational Neuroscience·Ahmed Abdelhameed, Magdy Bayoumi
Jun 4, 2021·BMC Bioinformatics·Shiu KumarAlok Sharma
May 28, 2021·Cognitive Neurodynamics·Zhongke GaoMatjaž Perc
Jun 12, 2021·BMC Bioinformatics·Shiu KumarAlok Sharma
Aug 27, 2021·Frontiers in Human Neuroscience·Xiyu SongBin Yan
Sep 11, 2021·International Journal for Numerical Methods in Biomedical Engineering·Ali A KhalilFathi E Abd El-Samie

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Resnet
VGGNet
ResNet152
Googlenet
Inception
PSDED
ImageNet
OHEM

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