An effective algorithm for the generation of patient-specific Purkinje networks in computational electrocardiology

Abstract

The Purkinje network is responsible for the fast and coordinated distribution of the electrical impulse in the ventricle that triggers its contraction. Therefore, it is necessary to model its presence to obtain an accurate patient-specific model of the ventricular electrical activation. In this paper, we present an efficient algorithm for the generation of a patient-specific Purkinje network, driven by measures of the electrical activation acquired on the endocardium. The proposed method provides a correction of an initial network, generated by means of a fractal law, and it is based on the solution of Eikonal problems both in the muscle and in the Purkinje network. We present several numerical results both in an ideal geometry with synthetic data and in a real geometry with patient-specific clinical measures. These results highlight an improvement of the accuracy provided by the patient-specific Purkinje network with respect to the initial one. In particular, a cross-validation test shows an accuracy increase of 19% when only the 3% of the total points are used to generate the network, whereas an increment of 44% is observed when a random noise equal to 20% of the maximum value of the clinical data is added to the measures.