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Discriminating Bipolar Disorder from Major Depression Based on Kernel Svm Using Functional Independent Components
Shuang Gao; Elizabeth A Osuch; Michael Wammes; Jean Théberge; Tianzi Jiang; Vince D Calhoun; Sui Jing(隋婧)
2017
会议名称2017 IEEE 27th International Workshop on Machine Learning for Signal Processing(MLSP 2017)
会议日期2017/9/25-28
会议地点Tokyo, Japan.
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.
关键词Independent Component Analysis Linear Subspace Kernel Svm Bipolar Disorder Major Depression Disorder Fmri Data Schizophrenia Unipolar Amygdala
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20794
专题脑网络组研究中心
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],2017.
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