A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network
BackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1160534/full |
| _version_ | 1797792321273593856 |
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| author | Ruofan Wang Haodong Wang Lianshuan Shi Chunxiao Han Qiguang He Yanqiu Che Li Luo |
| author_facet | Ruofan Wang Haodong Wang Lianshuan Shi Chunxiao Han Qiguang He Yanqiu Che Li Luo |
| author_sort | Ruofan Wang |
| collection | DOAJ |
| description | BackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.ObjectiveThis study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.MethodsFirst, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.ResultsFinally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks. |
| first_indexed | 2024-03-13T02:31:30Z |
| format | Article |
| id | doaj.art-d16041773bd448e3b0a9913562ce2a31 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-03-13T02:31:30Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-d16041773bd448e3b0a9913562ce2a312023-06-29T14:30:28ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652023-06-011510.3389/fnagi.2023.11605341160534A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional networkRuofan Wang0Haodong Wang1Lianshuan Shi2Chunxiao Han3Qiguang He4Yanqiu Che5Li Luo6School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaTianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaTianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, ChinaCollege of Agronomy, Sichuan Agricultural University, Chengdu, ChinaBackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.ObjectiveThis study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.MethodsFirst, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.ResultsFinally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.https://www.frontiersin.org/articles/10.3389/fnagi.2023.1160534/fullAlzheimer's diseaseEEGcomplex networkmulti-objective optimizationfeature selection |
| spellingShingle | Ruofan Wang Haodong Wang Lianshuan Shi Chunxiao Han Qiguang He Yanqiu Che Li Luo A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network Frontiers in Aging Neuroscience Alzheimer's disease EEG complex network multi-objective optimization feature selection |
| title | A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network |
| title_full | A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network |
| title_fullStr | A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network |
| title_full_unstemmed | A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network |
| title_short | A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network |
| title_sort | novel framework of mopso gdm in recognition of alzheimer s eeg based functional network |
| topic | Alzheimer's disease EEG complex network multi-objective optimization feature selection |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1160534/full |
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