Fei He

Fei He
Coventry University | CU · Department of Computing

PhD in Systems & Control Engineering
Senior Lecturer in Computational Biology & Neuroscience

About

48
Publications
9,022
Reads
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486
Citations
Introduction
My research has focused on developing nonlinear systems identification, signal processing and statistical machine learning approaches to study the complex nonlinear interactions in human brain network and diagnosis of neurological disorders (e.g. Alzheimer’s disease, epilepsy, tremor), as well as complex regulatory mechanisms in cellular (metabolic, genetic) networks. You can find the latest news of my group at https://feihelab.github.io
Additional affiliations
April 2017 - September 2018
Imperial College London
Position
  • Research Associate
Description
  • I was a member of Theoretical Systems Biology group.

Publications

Publications (48)
Preprint
Full-text available
Alzheimer's disease (AD) is the leading form of dementia in the world. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly...
Preprint
Full-text available
Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation. We address this limitat...
Article
Full-text available
Background Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as n:m phase locking value (where two coupling frequencies are linked as: mf1=nf2) and bi-phase locking value have previously been proposed to quantify phase coupling between two resona...
Preprint
Full-text available
This paper introduces a novel EEG channel selection method to determine which channel interrelationships provide the best classification accuracy between a group of patients with Alzheimer's disease (AD) and a cohort of age matched healthy controls (HC). Thereby, determine which inter-relationships are more important for the in-depth dynamical anal...
Article
Full-text available
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG w...
Preprint
Full-text available
Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation fro...
Article
The importance of an optimal solution for disaster evacuation has recently raised attention from researchers across multiple disciplines. This is not only a serious, but also a challenging task due to the complexities of the evacuees’ behaviors, route planning, and demanding coordination services. Although existing studies have addressed these chal...
Preprint
Full-text available
Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as $n:m$ phase locking value and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e.g. f, 2f/3) and across three frequencies (e.g....
Preprint
Full-text available
The human nervous system is one of the most complicated systems in nature. The complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and inpu...
Preprint
Full-text available
In this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer's disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features...
Article
Full-text available
Motivation: Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often lim...
Article
Full-text available
One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis (HRA) is a powerful tool to study this regulation at the metabolic, gene-expression and signaling levels. It has been widely applied to study the steady-state regulation; but analysis of the metabolic dynamics remains c...
Article
Full-text available
Background Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. We limit our analysis to nonlinear differential equation based inference methods. In order to avoid the compu...
Article
Full-text available
Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods:...
Preprint
Full-text available
Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. To avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient mat...
Article
Objective To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). Methods We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroe...
Article
Introduction Quantitative Electroencephalography (qEEG) has been shown to distinguish AD patients from healthy controls (HC) at a group but not at an individual level. A novel qEEG data analysis method created at the University of Sheffield can measure linear and non-linear levels of brain synchronisation in the time domain. Methods Patients with...
Article
Full-text available
Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of Information and Communication Technology (ICT): they receive information from the outside and inside of cells, integrate and inter...
Conference Paper
Selective catalytic reduction (SCR) system is a complex chemical process which is used to treat exhaust gas in many applications, e.g. diesel engines in automobiles. An SCR model was constructed by General Motors (GM) and the problem considered was to estimate the unknown kinetic parameters on-line taking into account aging effects. There are a num...
Chapter
Systems Biology brings the potential to discover fundamental principles of Life that cannot be discovered by considering individual molecules. This chapter discusses a number of early, more recent and upcoming discoveries of such network principles. These range from the balancing of fluxes through metabolic networks, the potential of those networks...
Article
Full-text available
Spectral measures of linear Granger causality have been widely applied to study the causal connectivity between time series data in neuroscience, biology, and economics. Traditional Granger causality measures are based on linear autoregressive with exogenous (ARX) inputs models of time series data, which cannot truly reveal nonlinear effects in the...
Article
Full-text available
Metabolic control analysis (MCA) and supply--demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply--demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), h...
Article
Full-text available
This paper introduces a new approach for nonlinear and non-stationary (time-varying) system identification based on time-varying nonlinear autoregressive moving average with exogenous variable (TV-NARMAX) models. The challenging model structure selection and parameter tracking problems are solved by combining a multiwavelet basis function expansion...
Article
Full-text available
A new frequency domain analysis framework for nonlinear time-varying systems is introduced based on parametric time-varying NARX models. It is shown how the time-varying effects can be mapped to the generalized frequency response functions to track nonlinear features in frequency, such as inter-modulation and energy transfer effects. A new mapping...
Article
A new NARX-based Granger linear and nonlinear casual influence detection method is presented in this paper to address the potential for linear and nonlinear models in data with applications to human EEG data analysis. Considering two signals initially, the paper introduces four indexes to measure the linearity and nonlinearity of a single signal, a...
Article
Full-text available
The purpose of model-based experimental design is to maximise the information gathered for quantitative model identification. Instead of the commonly used optimal experimental design, robust experimental design aims to address parametric uncertainties in the design process. In this paper, the Bayesian robust experimental design is investigated, whe...
Conference Paper
The purpose of model-based experimental design is to maximise the information gathered for quantitative model identification. Instead of the commonly used optimal experimental design, robust experimental design aims to address parametric uncertainties in the design process. In this paper, the Bayesian robust experimental design is investigated, whe...
Article
Full-text available
Experimental design is important in system identification, especially when the models are complex and the measurement data are sparse and noisy, as often occurs in modelling of biochemical regulatory networks. The quality of conventional optimal experimental design largely depends on the accuracy of model parameter estimation, which is often either...
Article
Full-text available
An important aspect of systems biology research is the so-called "reverse engineering" of cellular metabolic dynamics from measured input-output data. This allows researchers to estimate and validate both the pathway's structure as well as the kinetic constants. In this paper, the recently published 'Proximate Parameter Tuning' (PPT) method for the...
Article
Experimental design for cellular networks based on sensitivity analysis is studied in this work. Both optimal and robust experimental design strategies are developed for the IkappaB-NF-kappaB signal transduction model. Based on local sensitivity analysis, the initial IKK intensity is calculated using an optimal experimental design process, and seve...
Conference Paper
An important aspect of systems biology research is the so-called ldquoreverse engineeringrdquo of cellular metabolic dynamics from measured input-output data. This allows researchers to estimate and validate both the pathwaypsilas structure as well as the kinetic constants. In this paper, a regularization based method which performs model structure...
Conference Paper
Full-text available
Due to the general lack of experimental data for biochemical pathway model identification, cell-level time series experimental design is particularly important in current systems biology research. This paper investigates the problem of experimental design for signal transduction pathway modeling, and in particular, focuses on methods for parametric...
Chapter
Based on much experimentation, traditional biochemists and molecular biologists have developed many qualitative models and hypotheses for biochemical pathway study [7, 26, 28]. However, in order to evaluate the completeness and usefulness of a hypothesis, produce predictions for further testing, and better understand the interaction and dynamic of...
Article
Full-text available
As a general lack of quantitative measurement data for pathway modelling and parameter identification process, time-series experimental design is particularly important in current systems biology research. This paper mainly investigates state measurement/observer selection problem when parametric uncertainties are considered. Based on the extension...
Conference Paper
Full-text available
Parameter estimation of non-linear differential equations has long been an active and challenge research area. Conventionally methods are computationally intensive and often poorly conditioned. In the context of biochemical pathway modeling, a new method focused on this paper is the so-called "collocation" method, which is a nonparametric data smoo...
Article
Full-text available
This paper presents the importance of formulating general discrete-time model representations for current pathway deterministic modeling study. Discrete-time models can be considered as a link between continuous-time kinetic reactions and discrete-time experimentation as well as computer based simulation and analysis. In the paper, different discre...
Conference Paper
This paper presents the importance of formulating general discrete-time model representations for current pathway modeling study. Discrete-time models can be considered as a link between continuous-time kinetic reactions and discrete-time experimentation as well as computer based simulation and analysis. In the paper, different discretization techn...

Projects

Projects (2)
Project
Call for participation: special issue (Research Topic) of Frontiers in Computational Neuroscience & Frontiers in Neuroinformatics. More details see: https://www.frontiersin.org/research-topics/20200/nonlinear-connectivity-causality-and-information-processing-in-neuroscience Abstract submission deadline: 15 June 2021
Project
Develop novel nonlinear dynamic model-based time and frequency-domain (connectivity/causality) analysis tools for the detection and diagnostics of important neurological diseases/disorders, such as Alzheimer's and Parkinson's disease, Tremor, Epilepsy.