95 citations · 155 across the 5 of their papers we have counts for
5 papers
Characterisation of Anti-Arrhythmic Drug Effects on Cardiac Electrophysiology using Physics-Informed Neural Networks
Ching-En Chiu, Arieh Levy Pinto, Rasheda A Chowdhury +2
The ability to accurately infer cardiac electrophysiological (EP) properties is key to improving arrhythmia diagnosis and treatment. In this work, we developed a physics-informed n…
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients
Jipeng Yan, Biao Huang, Johanna Tonko +9
Micro-vascular flow in the myocardium is of significant importance clinically but remains poorly understood. Up to 25% of patients with symptoms of coronary heart diseases have no…
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…
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…