94 citations · 244 across the 9 of their papers we have counts for
9 papers
Estimation of fibre architecture and scar in myocardial tissue using electrograms: an in-silico study
Konstantinos Ntagiantas, Eduardo Pignatelli, Nicholas S. Peters +3
Atrial Fibrillation (AF) is characterized by disorganised electrical activity in the atria and is known to be sustained by the presence of regions of fibrosis (scars) or functional…
EP-PINNs: Cardiac Electrophysiology Characterisation using Physics-Informed Neural Networks
Clara Herrero Martin, Alon Oved, Rasheda A Chowdhury +4
Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of a…
In-Ear Measurement of Blood Oxygen Saturation: An Ambulatory Tool Needed To Detect The Delayed Life-Threatening Hypoxaemia in COVID-19
Harry J. Davies, Ian Williams, Nicholas S. Peters +1
Non-invasive ambulatory estimation of blood oxygen saturation has emerged as an important clinical requirement to detect hypoxemia in the delayed post-infective phase of COVID-19,…
Understanding the transition from paroxysmal to persistent atrial fibrillation from micro-anatomical re-entry in a simple model
Alberto Ciacci, Max Falkenberg, Kishan A. Manani +3
Atrial fibrillation (AF) is the most common cardiac arrhytmia, characterised by the chaotic motion of electrical wavefronts in the atria. In clinical practice, AF is classified und…
Unified Mechanism of Atrial Fibrillation in a Simple Model
Max Falkenberg, Andrew J. Ford, Anthony C. Li +3
The mechanism of atrial fibrillation (AF) is poorly understood, resulting in disappointing success rates of ablative treatment. Different mechanisms defined largely by different at…
Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling
Chris D. Cantwell, Yumnah Mohamied, Konstantinos N. Tzortzis +6
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibril…
Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation
Max Falkenberg McGillivray, William Cheng, Nicholas S. Peters +1
Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation. Using a simple cellular automata model of atrial fi…
Myocardial Architecture and Patient Variability in Clinical Patterns of Atrial Fibrillation
Kishan A. Manani, Kim Christensen, Nicholas S. Peters
Atrial fibrillation (AF) increases the risk of stroke by a factor of four to five and is the most common abnormal heart rhythm. The progression of AF with age, from short self-term…
A Simple Model for Identifying Critical Structures in Atrial Fibrillation
Kim Christensen, Kishan A. Manani, Nicholas S. Peters
Atrial fibrillation (AF) is the most common abnormal heart rhythm and the single biggest cause of stroke. Ablation, destroying regions of the atria, is applied largely empirically…