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dc.contributor.author
Chen, Xuning  
dc.contributor.author
Li, Binghua  
dc.contributor.author
Jia, Hao  
dc.contributor.author
Feng, Fan  
dc.contributor.author
Duan, Feng  
dc.contributor.author
Sun, Zhe  
dc.contributor.author
Caiafa, César Federico  
dc.contributor.author
Solé Casals, Jordi  
dc.date.available
2023-11-01T17:57:57Z  
dc.date.issued
2022-07  
dc.identifier.citation
Chen, Xuning; Li, Binghua; Jia, Hao; Feng, Fan; Duan, Feng; et al.; Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis; Frontiers Media; Frontiers in Neuroscience; 16; 7-2022; 1-12  
dc.identifier.issn
1662-453X  
dc.identifier.uri
http://hdl.handle.net/11336/216799  
dc.description.abstract
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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Frontiers Media  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
BRAINNETCNN  
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GEMD  
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GIFTED CHILDREN  
dc.subject
MRI  
dc.subject
STRUCTURAL CONNECTIVITY  
dc.subject.classification
Ciencias de la Información y Bioinformática  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-11-01T15:35:04Z  
dc.journal.volume
16  
dc.journal.pagination
1-12  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Chen, Xuning. Nankai University; China  
dc.description.fil
Fil: Li, Binghua. Nankai University; China  
dc.description.fil
Fil: Jia, Hao. Nankai University; China  
dc.description.fil
Fil: Feng, Fan. Nankai University; China  
dc.description.fil
Fil: Duan, Feng. Nankai University; China  
dc.description.fil
Fil: Sun, Zhe. No especifíca;  
dc.description.fil
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina  
dc.description.fil
Fil: Solé Casals, Jordi. Nankai University; China  
dc.journal.title
Frontiers in Neuroscience  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.3389/fnins.2022.866735