Richard H Clayton
Richard H Clayton
Insigneo institute for in-silico medicine, University of Sheffield
Verifierad e-postadress på sheffield.ac.uk
Citeras av
Citeras av
Models of cardiac tissue electrophysiology: progress, challenges and open questions
RH Clayton, O Bernus, EM Cherry, H Dierckx, FH Fenton, L Mirabella, ...
Progress in biophysics and molecular biology 104 (1-3), 22-48, 2011
Evidence for multiple mechanisms in human ventricular fibrillation
MP Nash, A Mourad, RH Clayton, PM Sutton, CP Bradley, M Hayward, ...
Circulation 114 (6), 536-542, 2006
Verification of cardiac tissue electrophysiology simulators using an N-version benchmark
SA Niederer, E Kerfoot, AP Benson, MO Bernabeu, O Bernus, C Bradley, ...
Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2011
A guide to modelling cardiac electrical activity in anatomically detailed ventricles
RH Clayton, AV Panfilov
Progress in biophysics and molecular biology 96 (1-3), 19-43, 2008
Effects of aerobic exercise training and yoga on the baroreflex in healthy elderly persons
AJ Bowman, RH Clayton, A Murray, JW Reed, MMF Subhan, GA Ford
European journal of clinical investigation 27 (5), 443-449, 1997
Whole heart action potential duration restitution properties in cardiac patients: a combined clinical and modelling study
MP Nash, CP Bradley, PM Sutton, RH Clayton, P Kallis, MP Hayward, ...
Experimental physiology 91 (2), 339-354, 2006
Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?
RH Johnstone, ETY Chang, R Bardenet, TP De Boer, DJ Gavaghan, ...
Journal of molecular and cellular cardiology 96, 49-62, 2016
Uncertainty and variability in computational and mathematical models of cardiac physiology
GR Mirams, P Pathmanathan, RA Gray, P Challenor, RH Clayton
The Journal of physiology 594 (23), 6833-6847, 2016
Phase singularities and filaments: simplifying complexity in computational models of ventricular fibrillation
RH Clayton, EA Zhuchkova, AV Panfilov
Progress in biophysics and molecular biology 90 (1-3), 378-398, 2006
Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG
RH Clayton, A Murray, RWF Campbell
Medical and Biological Engineering and Computing 31, 111-117, 1993
Recognition of ventricular fibrillation using neural networks
RH Clayton, A Murray, RWF Campbell
Medical and Biological Engineering and Computing 32, 217-220, 1994
Organization of ventricular fibrillation in the human heart: experiments and models
KHWJ Ten Tusscher, A Mourad, MP Nash, RH Clayton, CP Bradley, ...
Experimental physiology 94 (5), 553-562, 2009
Computational models of normal and abnormal action potential propagation in cardiac tissue: linking experimental and clinical cardiology
RH Clayton
Physiological Measurement 22 (3), R15, 2001
Bayesian sensitivity analysis of a cardiac cell model using a Gaussian process emulator
ETY Chang, M Strong, RH Clayton
PloS one 10 (6), e0130252, 2015
Developing a novel comprehensive framework for the investigation of cellular and whole heart electrophysiology in the in situ human heart: historical perspectives, current …
P Taggart, M Orini, B Hanson, M Hayward, R Clayton, H Dobrzynski, ...
Progress in biophysics and molecular biology 115 (2-3), 252-260, 2014
Regional differences in APD restitution can initiate wavebreak and re-entry in cardiac tissue: a computational study
RH Clayton, P Taggart
Biomedical engineering online 4, 1-14, 2005
Propagation of normal beats and re-entry in a computational model of ventricular cardiac tissue with regional differences in action potential shape and duration
RH Clayton, AV Holden
Progress in biophysics and molecular biology 85 (2-3), 473-499, 2004
Dispersion of cardiac action potential duration and the initiation of re-entry: a computational study
RH Clayton, AV Holden
Biomedical engineering online 4, 1-15, 2005
A method to quantify the dynamics and complexity of re-entry in computational models of ventricular fibrillation
RH Clayton, AV Holden
Physics in Medicine & Biology 47 (2), 225, 2002
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
M Mamalakis, AJ Swift, B Vorselaars, S Ray, S Weeks, W Ding, ...
Computerized Medical Imaging and Graphics 94, 102008, 2021
Systemet kan inte utföra åtgärden just nu. Försök igen senare.
Artiklar 1–20