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Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network
Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392428/ https://www.ncbi.nlm.nih.gov/pubmed/34439685 http://dx.doi.org/10.3390/brainsci11081066 |
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author | Li, Han Zhang, Qizhong Lin, Ziying Gao, Farong |
author_facet | Li, Han Zhang, Qizhong Lin, Ziying Gao, Farong |
author_sort | Li, Han |
collection | PubMed |
description | Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction. |
format | Online Article Text |
id | pubmed-8392428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83924282021-08-28 Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network Li, Han Zhang, Qizhong Lin, Ziying Gao, Farong Brain Sci Article Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction. MDPI 2021-08-13 /pmc/articles/PMC8392428/ /pubmed/34439685 http://dx.doi.org/10.3390/brainsci11081066 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Han Zhang, Qizhong Lin, Ziying Gao, Farong Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network |
title | Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network |
title_full | Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network |
title_fullStr | Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network |
title_full_unstemmed | Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network |
title_short | Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network |
title_sort | prediction of epilepsy based on tensor decomposition and functional brain network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392428/ https://www.ncbi.nlm.nih.gov/pubmed/34439685 http://dx.doi.org/10.3390/brainsci11081066 |
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