Search Results for author: Hernando Ombao

Found 10 papers, 2 papers with code

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification

no code implementations27 Jul 2021 Fuad Noman, Chee-Ming Ting, Hakmook Kang, Raphael C. -W. Phan, Brian D. Boyd, Warren D. Taylor, Hernando Ombao

Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.

Graph Embedding

Lattice Paths for Persistent Diagrams

no code implementations1 May 2021 Moo K. Chung, Hernando Ombao

We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations.

Topological Data Analysis

Smooth Online Parameter Estimation for time varying VAR models with application to rat's LFP data

no code implementations24 Feb 2021 Anass El Yaagoubi Bourakna, Marco Pinto, Norbert Fortin, Hernando Ombao

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

1 code implementation24 Jan 2021 Chee-Ming Ting, Jeremy I. Skipper, Steven L. Small, Hernando Ombao

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

no code implementations3 Jan 2021 Matheus B. Guerrero, Raphaël Huser, Hernando Ombao

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

no code implementations9 Apr 2020 Chee-Ming Ting, S. Balqis Samdin, Meini Tang, Hernando Ombao

We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time.

Community Detection Stochastic Block Model

Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network

no code implementations21 Mar 2019 Chun-Ren Phang, Chee-Ming Ting, Fuad Noman, Hernando Ombao

We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ).

EEG General Classification

Short-segment heart sound classification using an ensemble of deep convolutional neural networks

no code implementations27 Oct 2018 Fuad Noman, Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats.

General Classification

A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

no code implementations10 Sep 2018 Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, S. Balqis Samdin, Hernando Ombao, Hadri Hussain

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.

General Classification

Intrinsic data depth for Hermitian positive definite matrices

2 code implementations26 Jun 2017 Joris Chau, Hernando Ombao, Rainer von Sachs

Nondegenerate covariance, correlation and spectral density matrices are necessarily symmetric or Hermitian and positive definite.

Methodology 62G30, 62G15, 62G35, 62M15

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