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Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis
Gifted children and normal controls can be distinguished by analyzing the structural connectivity (SC) extracted from MRI data. Previous studies have improved classification accuracy by extracting several features of the brain regions. However, the limited size of the database may lead to degradatio...
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/PMC9295389/ https://www.ncbi.nlm.nih.gov/pubmed/35864986 http://dx.doi.org/10.3389/fnins.2022.866735 |
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author | Chen, Xuning Li, Binghua Jia, Hao Feng, Fan Duan, Feng Sun, Zhe Caiafa, Cesar F. Solé-Casals, Jordi |
author_facet | Chen, Xuning Li, Binghua Jia, Hao Feng, Fan Duan, Feng Sun, Zhe Caiafa, Cesar F. Solé-Casals, Jordi |
author_sort | Chen, Xuning |
collection | PubMed |
description | Gifted children and normal controls can be distinguished by analyzing the structural connectivity (SC) extracted from MRI data. Previous studies have improved classification accuracy by extracting several features of the brain regions. However, the limited size of the database may lead to degradation when training deep neural networks as classification models. To this end, we propose to use a data augmentation method by adding artificial samples generated using graph empirical mode decomposition (GEMD). We decompose the training samples by GEMD to obtain the intrinsic mode functions (IMFs). Then, the IMFs are randomly recombined to generate the new artificial samples. After that, we use the original training samples and the new artificial samples to enlarge the training set. To evaluate the proposed method, we use a deep neural network architecture called BrainNetCNN to classify the SCs of MRI data with and without data augmentation. The results show that the data augmentation with GEMD can improve the average classification performance from 55.7 to 78%, while we get a state-of-the-art classification accuracy of 93.3% by using GEMD in some cases. Our results demonstrate that the proposed GEMD augmentation method can effectively increase the limited number of samples in the gifted children dataset, improving the classification accuracy. We also found that the classification accuracy is improved when specific features extracted from brain regions are used, achieving 93.1% for some feature selection methods. |
format | Online Article Text |
id | pubmed-9295389 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92953892022-07-20 Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis Chen, Xuning Li, Binghua Jia, Hao Feng, Fan Duan, Feng Sun, Zhe Caiafa, Cesar F. Solé-Casals, Jordi Front Neurosci Neuroscience Gifted children and normal controls can be distinguished by analyzing the structural connectivity (SC) extracted from MRI data. Previous studies have improved classification accuracy by extracting several features of the brain regions. However, the limited size of the database may lead to degradation when training deep neural networks as classification models. To this end, we propose to use a data augmentation method by adding artificial samples generated using graph empirical mode decomposition (GEMD). We decompose the training samples by GEMD to obtain the intrinsic mode functions (IMFs). Then, the IMFs are randomly recombined to generate the new artificial samples. After that, we use the original training samples and the new artificial samples to enlarge the training set. To evaluate the proposed method, we use a deep neural network architecture called BrainNetCNN to classify the SCs of MRI data with and without data augmentation. The results show that the data augmentation with GEMD can improve the average classification performance from 55.7 to 78%, while we get a state-of-the-art classification accuracy of 93.3% by using GEMD in some cases. Our results demonstrate that the proposed GEMD augmentation method can effectively increase the limited number of samples in the gifted children dataset, improving the classification accuracy. We also found that the classification accuracy is improved when specific features extracted from brain regions are used, achieving 93.1% for some feature selection methods. Frontiers Media S.A. 2022-07-01 /pmc/articles/PMC9295389/ /pubmed/35864986 http://dx.doi.org/10.3389/fnins.2022.866735 Text en Copyright © 2022 Chen, Li, Jia, Feng, Duan, Sun, Caiafa and Solé-Casals. 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 | Neuroscience Chen, Xuning Li, Binghua Jia, Hao Feng, Fan Duan, Feng Sun, Zhe Caiafa, Cesar F. Solé-Casals, Jordi Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis |
title | Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis |
title_full | Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis |
title_fullStr | Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis |
title_full_unstemmed | Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis |
title_short | Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis |
title_sort | graph empirical mode decomposition-based data augmentation applied to gifted children mri analysis |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295389/ https://www.ncbi.nlm.nih.gov/pubmed/35864986 http://dx.doi.org/10.3389/fnins.2022.866735 |
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