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Exploration of the Human Purkinje Network in Virtual Populations

Lange, Matthias (2016) Exploration of the Human Purkinje Network in Virtual Populations. PhD thesis, University of Sheffield.

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Abstract

This thesis investigates the Purkinje network (PN) and its dependency on the heart shape (HS) through cardiac simulation on virtual populations (VPs). The heart is a complex organ and essential to the wellbeing of humans; its dysfunction is responsible for more than 27% of all deaths in the UK. The PN delivers the activation impulse to the ventricles of the heart and ensures their synchronous activation. Thus, the morphology of the PN is important, but it varies between species and in vivo imaging is not feasible. However, computer simulation could provide an alternative experimental tool. In simulation of the cardiac electrophysiology, the PN is often replaced by stimulus points on the HS that are fitted to physiological measurements (heart activation times, ECG). Thus, not allowing the study of the PN morphology, nor studies of arrhythmia involving re-entry into the PN. In this thesis, three studies involving explicit models of PNs have been conducted. First, an efficient algorithm for solving electrophysiology models for the PN is introduced. These allow performing simulations of physiological activations. To minimise the time for simulations, parallelisation with CPU and GPU architectures are investigated, which is of interest for VP studies. In the second study, false tendons (FTs) are studied, which provide an additional connection from the left bundle branch (LBB) and are potentially beneficial in case of LBB block. Therefore, the reduction in activation times by FT is studied as a function of the HS. In the third study, an automatically generated VP is used to explore uncertainty in the PN morphology. The conjecture is that the PN structure adapts to the HS. The coverage of the septum and the minimum distance of the PN to the base are varied. The features of the resulting ECG are used to find the PN that gives maximally synchronised contraction.

Item Type: Thesis (PhD)
Keywords: false tendon; Purkinje network; GPU computing; left bundle branch block;
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Electronic and Electrical Engineering (Sheffield)
Identification Number/EthosID: uk.bl.ethos.707473
Depositing User: Mr Matthias Lange
Date Deposited: 13 Apr 2017 13:47
Last Modified: 12 Oct 2018 09:37
URI: http://etheses.whiterose.ac.uk/id/eprint/16910

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