Javier Escudero

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Senior lecturer in biomedical signal processing

University of Edinburgh

United Kingdom

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About

Javier Escudero is a Senior Lecturer in Biomedical Signal Processing at the University of Edinburgh, United Kingdom. His research focuses on the analysis and interpretation of physiological signals, particularly in relation to cognitive impairment, seizure prediction, and artifact detection in medical data. Recent articles highlight his contributions to the fields of deep learning, network metrics, and physiological signal analysis.

Recent Grants

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Processing of brain time series to unravel changes in dynamic functional connectivity in disease

Open Date: 2020-09-30

Close Date: 2024-03-31

Grant: Close

Nonlinear analysis and modelling of multivariate signals on networks

Open Date: 2020-06-01

Close Date:

Grant: Close

Signal analysis of physiological recordings for decision support and outcome prediction in the paediatric intensive care unit

Open Date: 2018-08-31

Close Date: 2022-06-29

Grant: Close

TNT: Tracking Network dynamics with Tensor factorisations. Application to the human Chronnectome in Alzheimer's disease

Open Date: 2016-03-31

Close Date: 2017-04-29

Grant: Close

New objective methods to assess modularity in EEG functional networks during short-term memory binding in mild cognitive impairment

Open Date: 2016-02-08

Close Date: 2017-06-07

Articles (14)

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2025

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2022

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2022

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2022

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2021

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2021

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2021

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2021

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2020

Collaborators (2)

Alfredo Gonzalez-Sulser

University of Edinburgh

UNITED KINGDOM

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Daniel Abasolo

Senior Lecturer in Biomedical Engineering

University of Surrey

UNITED KINGDOM

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Javier Escudero

Research Interests

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About

Recent Grants

Articles (14)

Multiplex nodal modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task

VAE-IF: Deep feature extraction with averaging for fully unsupervised artifact detection in routinely acquired ICU time-series

CLaI: Collaborative Learning and Inference for low-resolution physiological signals – Validation in clinical event detection and prediction

Preictal period optimization for deep learning-based epileptic seizure prediction

Dispersion entropy for graph signals

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Abnormal brain state distribution and network connectivity in a SYNGAP1 rat model

Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps

Permutation Entropy for Graph Signals

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing

Impact of Visual Design Elements and Principles in Human Electroencephalogram Brain Activity Assessed with Spectral Methods and Convolutional Neural Networks

Higher order tensor decomposition for proportional myoelectric control based on muscle synergies

Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals

Examining the Relationship between Semiquantitative Methods Analysing Concentration-Time and Enhancement-Time Curves from Dynamic-Contrast Enhanced Magnetic Resonance Imaging and Cerebrovascular Dysfunction in Small Vessel Disease

Collaborators (2)

Alfredo Gonzalez-Sulser

Daniel Abasolo

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