Probabilistic learning of the Purkinje network from the electrocardiogram

TL;DR

This paper introduces a probabilistic method to identify the heart's Purkinje network from ECG data, enabling accurate, uncertainty-aware digital twin creation for personalized cardiology.

Contribution

It presents a novel probabilistic framework combining anatomical modeling, rule-based network generation, and Bayesian optimization to infer the Purkinje network from non-invasive ECG data.

Findings

Accurately recovers ECG signals in physiological and pathological cases.

Provides uncertainty estimates for Purkinje network parameters.

Demonstrates potential for personalized cardiac therapy planning.

Abstract

The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. Here, we propose a probabilistic approach for identifying the Purkinje network from non-invasive clinical data such as the standard electrocardiogram (ECG). We use cardiac imaging to build an anatomically accurate model of the ventricles; we algorithmically generate a rule-based Purkinje network tailored to the anatomy; we simulate physiological electrocardiograms with a fast model; we identify the geometrical and electrical parameters of the Purkinje-ECG model with Bayesian optimization and approximate Bayesian computation. The proposed approach is inherently probabilistic and generates a population of plausible Purkinje networks, all fitting the ECG within a given tolerance. In this way, we can estimate the uncertainty of the parameters, thus providing reliable predictions. We test our methodology in physiological and pathological scenarios, showing that we are able to accurately recover the ECG with our model. We propagate the uncertainty in the Purkinje network parameters in a simulation of conduction system pacing therapy. Our methodology is a step forward in creation of digital twins from non-invasive data in precision medicine. An open source implementation can be found at http://github.com/fsahli/purkinje-learning