Assessment of Cardiovascular Biomarkers Derived from Peripheral Pulse Waveforms Using Computational Blood Flow Modelling

Student thesis: Doctoral ThesisDoctor of Philosophy


The aim of this thesis is to investigate the ability of cardiovascular
biomarkers calculated from peripheral pulse waveforms to estimate central
properties of the cardiovascular system (e.g. aortic stiffness) using
nonlinear one-dimensional (1-D) modelling of pulse wave propagation
in the arterial network. To test these biomarkers, I have produced
novel 1-D models of pulse wave propagation under normal and pathological
conditions. In the first part of my thesis, I extended the modelling
capabilities of the existing 1-D/0-D code to represent arterial
blood ow under diabetes, hypertension, and combined diabetes and
hypertension. Cardiac and vascular parameters of the 1-D model were
tailored to best match data available in the literature to produce generalised
hypertensive, diabetic, and combined diabetic and hypertensive
population models. Using these models, I have shown that the pulse
waveform at the finger is strongly affected by the aortic
flow wave and the muscular artery stiffiness and diameter. Furthermore the peak to peak time measured from the pulse waveform at the finger can identify
hypertensive from diabetic patients.
In the second part, I developed a new methodology for optimising the
number of arterial segments in 1-D modelling required to simulate precisely
the blood pressure and flow waveforms at an arbitrary arterial
location. This is achieved by systematically lumping peripheral 1-D
model branches into 0-D models that preserve the net resistance and
total compliance of the original model. The methodology is important
to simplify the computational domain while maintaining the precision
of the numerical predictions { an important step to translate 1-D modelling
to the clinic.
This thesis provides novel computational tools of blood flow modelling
and waveform analysis for the design, development and testing of pulse
wave biomarkers. These tools may help bridge the gap between clinical
and computational approaches.
Date of Award2017
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorJordi Alastruey (Supervisor) & Philip Chowienczyk (Supervisor)

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