1
|
Chen X, Li B, Jia H, Feng F, Duan F, Sun Z, Caiafa CF, Solé-Casals J. Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis. Front Neurosci 2022; 16:866735. [PMID: 35864986 PMCID: PMC9295389 DOI: 10.3389/fnins.2022.866735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/27/2022] [Indexed: 12/05/2022] Open
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.
Collapse
Affiliation(s)
- Xuning Chen
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Binghua Li
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Hao Jia
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Fan Feng
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Feng Duan
- Department of Artificial Intelligence, Nankai University, Tianjin, China
- *Correspondence: Feng Duan
| | - Zhe Sun
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Saitama, Japan
- Zhe Sun
| | - Cesar F. Caiafa
- Department of Artificial Intelligence, Nankai University, Tianjin, China
- Instituto Argentino de Radioastronomía, Consejo Nacional de Investigaciones Científicas y Técnicas – Centro Científico Tecnológico La Plata/Comisión de Investigaciones Científicas – Provincia de Buenos Aires/Universidad Nacional de La Plata, Villa Elisa, Argentina
| | - Jordi Solé-Casals
- Department of Artificial Intelligence, Nankai University, Tianjin, China
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Spain
- Jordi Solé-Casals
| |
Collapse
|