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Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network
In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399419/ https://www.ncbi.nlm.nih.gov/pubmed/36032671 http://dx.doi.org/10.3389/fmolb.2022.931688 |
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author | Fan, Yuying Chen, Duo Wang , Hua Pan , Yijie Peng , Xueping Liu , Xueyan Liu, Yunhui |
author_facet | Fan, Yuying Chen, Duo Wang , Hua Pan , Yijie Peng , Xueping Liu , Xueyan Liu, Yunhui |
author_sort | Fan, Yuying |
collection | PubMed |
description | In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems. However, very few research studies have focused on the use of CNNs for BASED scoring, a challenging but vital task in the diagnosis and treatment of infantile spasms. This study proposes an automatic BASED scoring framework using EEG and a deep CNN. The feasibility of using CNN for automatic BASED scoring was investigated in 36 patients with infantile spasms by annotating their long-term EEG data with four levels of the BASED score (scores 5, 4, 3, and ≤2). In the validation set, the accuracy was 96.9% by applying a multi-layer CNN to classify the EEG data as a 4-label problem. The extensive experiments have demonstrated that our proposed approach offers high accuracy and, hence, is an important step toward an automatic BASED scoring algorithm. To the best of our knowledge, this is the first attempt to use a CNN to construct a BASED-based scoring model. |
format | Online Article Text |
id | pubmed-9399419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93994192022-08-25 Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network Fan, Yuying Chen, Duo Wang , Hua Pan , Yijie Peng , Xueping Liu , Xueyan Liu, Yunhui Front Mol Biosci Molecular Biosciences In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems. However, very few research studies have focused on the use of CNNs for BASED scoring, a challenging but vital task in the diagnosis and treatment of infantile spasms. This study proposes an automatic BASED scoring framework using EEG and a deep CNN. The feasibility of using CNN for automatic BASED scoring was investigated in 36 patients with infantile spasms by annotating their long-term EEG data with four levels of the BASED score (scores 5, 4, 3, and ≤2). In the validation set, the accuracy was 96.9% by applying a multi-layer CNN to classify the EEG data as a 4-label problem. The extensive experiments have demonstrated that our proposed approach offers high accuracy and, hence, is an important step toward an automatic BASED scoring algorithm. To the best of our knowledge, this is the first attempt to use a CNN to construct a BASED-based scoring model. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399419/ /pubmed/36032671 http://dx.doi.org/10.3389/fmolb.2022.931688 Text en Copyright © 2022 Fan, Chen, Wang , Pan , Peng , Liu and Liu. https://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 | Molecular Biosciences Fan, Yuying Chen, Duo Wang , Hua Pan , Yijie Peng , Xueping Liu , Xueyan Liu, Yunhui Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network |
title | Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network |
title_full | Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network |
title_fullStr | Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network |
title_full_unstemmed | Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network |
title_short | Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network |
title_sort | automatic based scoring on scalp eeg in children with infantile spasms using convolutional neural network |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399419/ https://www.ncbi.nlm.nih.gov/pubmed/36032671 http://dx.doi.org/10.3389/fmolb.2022.931688 |
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