Classification of temporal lobe epilepsy with and without hippocampal sclerosis via two-level feature selection
文献类型：会议论文
| 作者 | Wang Xin1 ; Ren Yanshuang2; Zhang Wensheng1
|
| 出版日期 | 2017 |
| 会议日期 | 2017-5 |
| 会议地点 | GuiLin, China |
| 英文摘要 | Hippocampal sclerosis (HS) is one of the most common histopathological abnormalities encountered in patients with temporal lobe epilepsy (TLE), which often serves as a diagnosis index of TLE. However, some patients with TLE have no pathologic characteristics of HS, which brings challenge to the diagnosis of TLE. Therefore, exploring effective methods to classify TLE patients with and without HS is meaningful to understanding the pathogenesis of TLE. In this paper, we propose a two-level feature selection method for classification. We select the categories of features as the first level and pick out the discriminating dimensions as the second level. Furthermore, we combine six regional brain characteristics as our features, including regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), regional functional connectivity strength (RFCS) and three graph-based features. Results show that our method yields higher classification performance compared against the classifiers with single feature and without any level feature selection using functional magnetic resonance imaging (fMRI) data. Moreover, the discriminative brain regions selected by our method are consistent with previous studies. Thus, our method can accurately classify TLE patients with and without HS, which is interpretable from the perspective of physiology at the same time. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14846]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Zhang Wensheng |
| 作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.Department of Radiology, Guang An Men Hospital of China Academy of Traditional Chinese Medicine |
| 推荐引用方式 GB/T 7714 | Wang Xin,Ren Yanshuang,Zhang Wensheng. Classification of temporal lobe epilepsy with and without hippocampal sclerosis via two-level feature selection[C]. 见:. GuiLin, China. 2017-5. |
入库方式： OAI收割
来源：自动化研究所
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