Discriminating Bipolar Disorder from Major Depression Based on Kernel Svm Using Functional Independent Components

	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
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20794
专题	脑图谱与类脑智能实验室_脑网络组研究
作者单位	Institute of Automation Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
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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