33 citations · 108 across the 70 of their papers we have counts for
8 papers · 1 filter
Statistical Model for Dynamically-Changing Correlation Matrices with Application to Brain Connectivity
Shih-Gu Huang, S. Balqis Samdin, Chee-Ming Ting +2
Background: Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain…
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
Fuad Noman, Chee-Ming Ting, Sh-Hussain Salleh +1
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We desig…
Modeling Brain Connectivity with Graphical Models on Frequency Domain
Xu Gao, Weining Shen, Chee-Ming Ting +3
Multichannel electroencephalograms (EEGs) have been widely used to study cortical connectivity during acquisition of motor skills. In this paper, we introduce copula Gaussian graph…
Modeling Dependence via Copula of Functionals of Fourier Coefficients
Charles Fontaine, Ron D. Frostig, Hernando Ombao
The goal of this paper is to develop a measure for characterizing complex dependence between stationary time series that cannot be captured by traditional measures such as correlat…
Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials
Charles Fontaine, Ron D. Frostig, Hernando Ombao
This paper intends to develop tools for characterizing non-linear spectral dependence between spontaneous brain signals. We use parametric copula models (both bivariate and vine mo…
Topological Brain Network Distances
Moo K. Chung, Hyekyoung Lee, Andrey Gritsenko +4
Existing brain network distances are often based on matrix norms. The element-wise differences in the existing matrix norms may fail to capture underlying topological differences.…