Classification of partial seizures based on functional connectivity: A MEG study with support vector machine
文献类型：期刊论文
| 作者 | Wang, Yingwei2; Li, Zhongjie3; Zhang, Yujin1,5 ; Long, Yingming2; Xie, Xinyan2; Wu, Ting2,4
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| 刊名 | FRONTIERS IN NEUROINFORMATICS
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| 出版日期 | 2022-08-18 |
| 卷号 | 16页码:14 |
| 关键词 | temporal lobe epilepsy resting-state functional connectivity MEG machine learning classification |
| DOI | 10.3389/fninf.2022.934480 |
| 通讯作者 | Wu, Ting(fsyy00598@njucm.edu.cn) |
| 英文摘要 | Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is divided into two subtypes, complex partial seizures (CPS) and simple partial seizures (SPS), based on clinical phenotypes. Revealing differences among the functional networks of different types of TLE can lead to a better understanding of the symbology of epilepsy. Whereas Although most studies had focused on differences between epileptic patients and healthy controls, the neural mechanisms behind the differences in clinical representations of CPS and SPS were unclear. In the context of the era of precision, medicine makes precise classification of CPS and SPS, which is crucial. To address the above issues, we aimed to investigate the functional network differences between CPS and SPS by constructing support vector machine (SVM) models. They mainly include magnetoencephalography (MEG) data acquisition and processing, construction of functional connectivity matrix of the brain network, and the use of SVM to identify differences in the resting state functional connectivity (RSFC). The obtained results showed that classification was effective and accuracy could be up to 82.69% (training) and 81.37% (test). The differences in functional connectivity between CPS and SPS were smaller in temporal and insula. The differences between the two groups were concentrated in the parietal, occipital, frontal, and limbic systems. Loss of consciousness and behavioral disturbances in patients with CPS might be caused by abnormal functional connectivity in extratemporal regions produced by post-epileptic discharges. This study not only contributed to the understanding of the cognitive-behavioral comorbidity of epilepsy but also improved the accuracy of epilepsy classification. |
| WOS关键词 | TEMPORAL-LOBE EPILEPSY ; COMPLEX PARTIAL SEIZURES ; OROALIMENTARY AUTOMATISMS ; OPERCULAR CORTEX ; BRAIN NETWORKS ; HIPPOCAMPAL ; ABSENCE ; EEG ; PROPAGATION ; PERFUSION |
| 资助项目 | National Natural Science Foundation of China ; [82172022] |
| WOS研究方向 | Mathematical & Computational Biology ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000861312600001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50452]  |
| 专题 | 自动化研究所_类脑智能研究中心 |
| 通讯作者 | Wu, Ting |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China 2.Nanjing Univ Chinese Med, Affiliated Hosp, Dept Radiol, Nanjing, Peoples R China 3.Tianjin Univ, Coll Intelligence & Comp, Tianjin Key Lab Cognit Comp & Applicat, Tianjin, Peoples R China 4.Nanjing Med Univ, Nanjing Brain Hosp, Dept Magnetoencephalog, Nanjing, Peoples R China 5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yingwei,Li, Zhongjie,Zhang, Yujin,et al. Classification of partial seizures based on functional connectivity: A MEG study with support vector machine[J]. FRONTIERS IN NEUROINFORMATICS,2022,16:14. |
| APA | Wang, Yingwei,Li, Zhongjie,Zhang, Yujin,Long, Yingming,Xie, Xinyan,&Wu, Ting.(2022).Classification of partial seizures based on functional connectivity: A MEG study with support vector machine.FRONTIERS IN NEUROINFORMATICS,16,14. |
| MLA | Wang, Yingwei,et al."Classification of partial seizures based on functional connectivity: A MEG study with support vector machine".FRONTIERS IN NEUROINFORMATICS 16(2022):14. |
入库方式： OAI收割
来源：自动化研究所
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