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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
Conference Name2017 IEEE 27th International Workshop on Machine Learning for Signal Processing(MLSP 2017)
Conference Date2017/9/25-28
Conference PlaceTokyo, Japan.
AbstractIn 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.
KeywordIndependent Component Analysis Linear Subspace Kernel Svm Bipolar Disorder Major Depression Disorder Fmri Data Schizophrenia Unipolar Amygdala
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20794
Collection脑网络组研究中心
AffiliationInstitute of Automation Chinese Academy of Sciences
Recommended Citation
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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