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Found 10 papers, 2 papers with code
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.
Lattice Paths for Persistent Diagrams
We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations.
Smooth Online Parameter Estimation for time varying VAR models with application to rat's LFP data
For these reasons we propose a new smooth online parameter estimation approach (SOPE) that has the ability to control for the smoothness of the estimates with a reasonable computational complexity.
Time Series
Methodology
Computation
Other Statistics
Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI
We propose a novel, data-driven approach based on low-rank plus sparse (L+S) decomposition to isolate stimulus-driven dynamic changes in brain functional connectivity (FC) from the background noise, by exploiting shared network structure among subjects receiving the same naturalistic stimuli.
Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data
Our proposed method, the conditional extremal dependence for brain connectivity (Conex-Connect), is a pioneering approach that links the association between extreme values of higher oscillations at a reference channel with the other brain network channels.
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time.
Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network
We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ).
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats.
A Markov-Switching Model Approach to Heart Sound Segmentation and Classification
Methods: We propose an approach based on Markov switching autoregressive model (MSAR) to segmenting the HS into four fundamental components each with distinct second-order structure.
Intrinsic data depth for Hermitian positive definite matrices
Nondegenerate covariance, correlation and spectral density matrices are necessarily symmetric or Hermitian and positive definite.
Methodology 62G30, 62G15, 62G35, 62M15
