Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling

TL;DR

This paper reviews recent machine learning and predictive modelling techniques applied to cardiac electrophysiology data, aiming to improve diagnosis and treatment of arrhythmias like atrial fibrillation through better analysis and prediction methods.

Contribution

It highlights novel machine learning approaches, including deep learning, for analyzing complex electrogram data and integrating predictive models to enhance diagnosis and personalized treatment of cardiac arrhythmias.

Findings

Machine learning offers new insights into complex electrogram data.

Predictive modelling improves future state prediction of cardiac systems.

Enhanced analysis methods could lead to more effective ablation strategies.

Abstract

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.