Normalisation of Action Potential Data Recorded with Sharp Electrodes Maximises Its Utility for Model Development

Barral, Yann Stanislas H.M.; Polonchuk, Liudmila; R. Mirams, Gary; Clerx, Michael; Page, Guy; Sweat, Katrina; Abi-Gerges, Najah; Wang, Ken; Gavaghan, David J.

Abstract

In silico models of cardiomyocyte electrophysiology describe the various ionic currents and fluxes that lead to the formation of action potentials (APs). Experimental data used to create such models can be recorded in adult human cardiac trabeculae using sharp electrodes. During these experiments, the stability of the electrode's position can not always be maintained, leading to spontaneous changes in the recorded voltage and to partial loss of data for model development. In this study, we explored the normalisation of APs recorded with sharp electrodes to reduce the impact of electrode movement on data quality. We show that APs normalised with peak voltage and resting membrane potential as reference points were identical before and after electrode movement, and can still be used for model development. Using a synthetic (simulated) dataset and the Tusscher & Panfilov 2006 model we show that normalising experimental AP traces does not significantly impact predictions of the model. We conclude that normalisation of APs increases the effective size of sharp-electrode datasets without compromising the identifiability and accuracy of inferred model parameters. In addition, our findings suggest that the electrophysiological activity of the recorded cardiac cells was not affected by the electrode's movement, and that changes in electrode offsets can explain the variations observed in the non-normalised recordings.

• Conference Name: 2022 Computing in Cardiology Conference
• Conference Location: Tampere, Finland
• Start Date: Sep 4, 2022
• End Date: Sep 7, 2022
• Acceptance Date: Jun 13, 2022
• Online Publication Date: Dec 31, 2022
• Publication Date: Dec 31, 2022
• Deposit Date: Oct 13, 2023
• Volume: 49
• Series Title: Computing in Cardiology
• Series ISSN: 2325-887X
• Book Title: Computing in Cardiology 2022
• DOI: https://doi.org/10.22489/cinc.2022.356
• Public URL: https://nottingham-repository.worktribe.com/output/20008105
• Publisher URL: https://ieeexplore.ieee.org/document/10081750