|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)
|Conference Name||2017 IEEE 27th International Workshop on Machine Learning for Signal Processing(MLSP 2017)
|Conference Place||Tokyo, Japan.
|Abstract||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.|
|Keyword||Independent Component Analysis
Major Depression Disorder
|Affiliation||Institute of Automation Chinese Academy of Sciences|
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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