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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...

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Autores principales: Chen, Xuning, Li, Binghua, Jia, Hao, Feng, Fan, Duan, Feng, Sun, Zhe, Caiafa, Cesar F., Solé-Casals, Jordi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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