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 flow 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 stiffness 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.
Version 2.0 (10/06/16)
The self-defined font is used, because 'Calibri' is
not supported in the latex font packages. 'LuaLatex'
should be used.
This template has been generated according to
the Power Point template of LUMC in 2016.
This is generated purely with images as the
The bullet point color was used purely for personal
Any more adding to the template are welcome.
In order to use the navigation bar, the title
for each section should not be to long.
Adding animation is possible. I prefer to add another
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Division Medical imaging processing,
Leiden University Medical Center
Generated by Shengnan Liu on 21-01-2016
Cleaned up for further usage on 10-06-2016
Template criado por Alexandre do Nascimento Silva (email@example.com)a partir da classe ABNT para uso das monografias de conclusão de curso da Faculdade ÁREA1 e modificado pelo professor Lázaro Silva (firstname.lastname@example.org) para uso na internet sem a necessidade de instalação.
This article proposes to obtain a statistical model of the daily peak electricity load of a household located in Austin-TX,USA. The Box-Jenkins methodology was followed to obtain the best fit for the time-series. Four models provided a good fit: ARIMA(0,1,2), ARIMA(1,1,2), SARIMA(0,1,2)(0,1,1) and SARIMA(1,1,2)(0,1,1). The model with the highest Akaike Information Criteria was the ARIMA(1,2,2). However, the model with the highest forecast accuracy was the SARIMA(1,1,2)(0,1,1), which obtained an RMSE of 0.296 and a MAPE Of 15.00.
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