Characterisation of Anti-Arrhythmic Drug Effects on Cardiac Electrophysiology using Physics-Informed Neural Networks

Understand

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 neural networks (PINNs) framework to predict how different myocardial EP parameters are modulated by anti-arrhythmic drugs.
• Using $\textit{in vitro}$ optical mapping images and the 3-channel Fenton-Karma model, we estimated the changes in ionic channel conductance caused by these drugs.
• Our framework successfully characterised the action of drugs HMR1556, nifedipine and lidocaine - respectively, blockade of $I_{K}$, $I_{Ca}$, and $I_{Na}$ currents - by estimating that they decreased the respective channel conductance by $31.8\pm2.7\%$ $(p=8.2 \times 10^{-5})$, $80.9\pm21.6\%$ $(p=0.02)$, and $8.6\pm0.5\%$ $ (p=0.03)$, leaving the conductance of other channels unchanged.