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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
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MRI
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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
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