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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7257380/ https://www.ncbi.nlm.nih.gov/pubmed/32528398 http://dx.doi.org/10.3389/fneur.2020.00375 |
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author | Gao, Yunyuan Gao, Bo Chen, Qiang Liu, Jia Zhang, Yingchun |
author_facet | Gao, Yunyuan Gao, Bo Chen, Qiang Liu, Jia Zhang, Yingchun |
author_sort | Gao, Yunyuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7257380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72573802020-06-10 Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification Gao, Yunyuan Gao, Bo Chen, Qiang Liu, Jia Zhang, Yingchun Front Neurol Neurology 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. Frontiers Media S.A. 2020-05-22 /pmc/articles/PMC7257380/ /pubmed/32528398 http://dx.doi.org/10.3389/fneur.2020.00375 Text en Copyright © 2020 Gao, Gao, Chen, Liu and Zhang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Gao, Yunyuan Gao, Bo Chen, Qiang Liu, Jia Zhang, Yingchun Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification |
title | Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification |
title_full | Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification |
title_fullStr | Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification |
title_full_unstemmed | Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification |
title_short | Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification |
title_sort | deep convolutional neural network-based epileptic electroencephalogram (eeg) signal classification |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7257380/ https://www.ncbi.nlm.nih.gov/pubmed/32528398 http://dx.doi.org/10.3389/fneur.2020.00375 |
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