325 citations · 748 across the 26 of their papers we have counts for
27 papers
Dispersion entropy: A Measure of Irregularity for Graph Signals
John Stewart Fabila-Carrasco, Chao Tan, Javier Escudero
We introduce a novel method, called Dispersion Entropy for Graph Signals, , as a powerful tool for analysing the irregularity of signals defined on graphs. We demonstrate the…
A noise-robust Multivariate Multiscale Permutation Entropy for two-phase flow characterisation
John Stewart Fabila-Carrasco, Chao Tan, Javier Escudero
Using a graph-based approach, we propose a multiscale permutation entropy to explore the complexity of multivariate time series over multiple time scales. This multivariate multisc…
Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU
Hollan Haule, Evangelos Kafantaris, Tsz-Yan Milly Lo +2
Physiological monitoring in intensive care units (ICU) generates data that can be used in clinical research. However, the recording conditions in clinical settings limit the automa…
Multivariate permutation entropy, a Cartesian graph product approach
John Stewart Fabila-Carrasco, Chao Tan, Javier Escudero
Entropy metrics are nonlinear measures to quantify the complexity of time series. Among them, permutation entropy is a common metric due to its robustness and fast computation. Mul…
Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis
Evangelos Kafantaris, Tsz-Yan Milly Lo, Javier Escudero
Multivariate entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysi…
Permutation Entropy for Graph Signals
John Stewart Fabila-Carrasco, Chao Tan, Javier Escudero
Entropy metrics (for example, permutation entropy) are nonlinear measures of irregularity in time series (one-dimensional data). Some of these entropy metrics can be generalised to…
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
Ali H. Al-Timemy, Rami N. Khushaba, Zahraa M. Mosa +1
Clinicians in the frontline need to assess quickly whether a patient with symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated in low resource settings t…
On the use of higher-order tensors to model muscle synergies
Ahmed Ebied, Loukianos Spyrou, Eli Kinney-Lang +1
The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current mu…
Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control
Ahmed Ebied, Eli Kinney-Lang, Javier Escudero
In recent years, muscle synergies have been pro-posed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matr…
Multiscale Fluctuation-based Dispersion Entropy and its Applications to Neurological Diseases
Hamed Azami, Steven E. Arnold, Saeid Sanei +4
Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuatio…
Higher order tensor decomposition for proportional myoelectric control based on muscle synergies
Ahmed Ebied, Eli Kinney-Lang, Javier Escudero
Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is lim…
Muscle Activity Analysis using Higher-Order Tensor Models: Application to Muscle Synergy Identification
Ahmed Ebied, Eli Kinney-lang, Loukianos Spyrou +1
Higher-order tensor decompositions have hardly been used in muscle activity analysis despite multichannel electromyography (EMG) datasets naturally occurring as multi-way structure…
Normalised Degree Variance
Keith M. Smith, Javier Escudero
Finding graph indices which are unbiased to network size and density is of high importance both within a given field and across fields for enhancing comparability of modern network…
Tensor-driven extraction of developmental features from varying paediatric EEG datasets
Eli Kinney-Lang, Loukianos Spyrou, Ahmed Ebied +2
Objective. Consistently changing physiological properties in developing children's brains challenges new data heavy technologies, like brain-computer interfaces (BCI). Advancing si…
Amplitude- and Frequency-based Dispersion Patterns and Entropy
Hamed Azami, Javier Escudero
Permutation patterns-based approaches, such as permutation entropy (PerEn), have been widely and successfully used to analyze data. However, these methods have two main shortcoming…
Complex tensor factorisation with PARAFAC2 for the estimation of brain connectivity from the EEG
Loukianos Spyrou, Mario Parra, Javier Escudero
Objective: The coupling between neuronal populations and its magnitude have been shown to be informative for various clinical applications. One method to estimate brain connectivit…
Weighted network estimation by the use of topological graph metrics
Loukianos Spyrou, Javier Escudero
Topological metrics of graphs provide a natural way to describe the prominent features of various types of networks. Graph metrics describe the structure and interplay of graph edg…
Multivariate Multiscale Dispersion Entropy of Biomedical Times Series
Hamed Azami, Alberto Fernandez, Javier Escudero
Objective: Due to the non-linearity of numerous biomedical signals, non-linear analysis of multi-channel time series, notably multivariate multiscale entropy (mvMSE), has been exte…
Are Erdös- Rényi Random Graphs Topologically Random?
Keith Smith, Javier Escudero
No. We prove that Erdos- Renyi Random Graphs are not topologically random.
Graph-Variate Signal Analysis
Keith Smith, Loukianos Spyrou, Javier Escudero
Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research…
Accounting for the Complex Hierarchical Topology of EEG Phase-Based Functional Connectivity in Network Binarisation
Keith Smith, Daniel Abasalo, Javier Escudero
Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG phase-based functio…
Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy
Keith Smith, Benjamin Ricaud, Nauman Shahid +6
Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first stud…
Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals
Hamed Azami, Mostafa Rostaghi, Daniel Abasolo +1
Multiscale entropy (MSE) is a widely-used tool to analyze biomedical signals. It was proposed to overcome the deficiencies of conventional entropy methods when quantifying the comp…
Comparison of Network Analysis Approaches on EEG Connectivity in Beta during Visual Short-Term Memory Binding Tasks
Keith Smith, Hamed Azami, Mario A. Parra +2
We analyse the electroencephalogram signals in the beta band of working memory representation recorded from young healthy volunteers performing several different Visual Short-Term…