A Two-Stage Purkinje Network for More Accurate ECG Representations

Abstract

<jats:title>Abstract</jats:title> <jats:p>In this manuscript we propose a method to generate Purkinje networks that are anatomically and physiologically plausible, for use with in-silico modeling. Purkinje networks play a fundamental role in shaping cardiac electrical activation patterns and their corresponding clinical electrocardiograms (ECGs). Despite a known variability in ventricular activation sequences, certain sites of early activation within the left and right ventricles have been identified in the literature for normal electrical excitation patterns. Nevertheless, in-vivo imaging of Purkinje networks cannot at present yield detailed information on their structure, so there is a genuine need for in-silico models that can construct Purkinje networks that are both anatomically and physiologically plausible, in particular networks that can exhibit correctly situated early activation sites in the ventricles. Of special interest to this manuscript is the method of representation of Purkinje networks by line-like electrical elements that are generated by means of a fractal-tree algorithm (1–3) to overlay the irregular endocardial surfaces. A known drawback to a direct implementation of this approach in complex geometries relates to its incorrect modeling of clinically observed ECGs and electrical activation sequences for a human heart (4). Our aim was thus to correct this deficiency by generating Purkinje networks that leverage a pre-knowledge of the location of early activation sites. At every such site we first generate a Purkinje sub-network. These sub-networks are linked together and to the bundle of His, setting up our first stage of the Purkinje network. Subsequently, we spawn a second stage to the Purkinje network from one or more tips of any given sub-network, to cover the full endocardial surface with Purkinje elements. Our resulting activation sequences and ECGs compare favorably to those of a population of 39 healthy male individuals (the PTB diagnostic database), and our corresponding mechanical markers of cardiac function also match well with the literature.</jats:p>

Development of electrocardiogram intelligent and wearable monitoring system-assisting in care

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