Discriminating Bipolar Disorder from Major Depression Based on Kernel Svm Using Functional Independent Components
文献类型：会议论文
| 作者 | Shuang Gao ; Elizabeth A Osuch; Michael Wammes; Jean Théberge; Tianzi Jiang; Vince D Calhoun; Sui Jing(隋婧)
|
| 出版日期 | 2017 |
| 会议日期 | 2017/9/25-28 |
| 会议地点 | Tokyo, Japan. |
| 关键词 | Independent Component Analysis Linear Subspace Kernel Svm Bipolar Disorder Major Depression Disorder Fmri Data Schizophrenia Unipolar Amygdala |
| 英文摘要 | In this paper we describe a deconvolution technique for estimation of the neuronal signal from an observed hemodynamic responses in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Additionally, we enhance the cubature Kalman filter with a variational Bayesian approach for adaptive estimation of the measurement noise covariance. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20794]  |
| 专题 | 自动化研究所_脑网络组研究中心 |
| 作者单位 | Institute of Automation Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Shuang Gao,Elizabeth A Osuch,Michael Wammes,et al. Discriminating Bipolar Disorder from Major Depression Based on Kernel Svm Using Functional Independent Components[C]. 见:. Tokyo, Japan.. 2017/9/25-28. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。