Model-based Algorithm Development with Focus on Biosignal Processing
Yao
Yu
Yao, Yu
aut
2015-06-24
2018-01-22
en
<p>In recent years, the development of cheap and robust sensors
combined with the ever increasing availability of the internet
led to a revolution in information technology, giving rise to an
amount of data, which was unimaginable just a decade ago. This
explosion in data lead to an increased demand for algorithms
for processing this data. However, an often overlooked aspect
is that with ever sophisticated algorithms there is associated a
demand for equally sophisticated mathematical modelling. In
this thesis, we explore the interaction between algorithm design
and modelling.
<br> Although, the models and methods discussed here are not
limited to any single domain of application, we will base our discussion
on example applications from the domain of biomedical
engineering. This is because the analysis of physiological time
series is characterised by two problems which help to highlight
the importance of modelling. First, the high noise level of biological
signals requires strong regularization, which can be provided
via a model. Second, in many medical applications the value of
interest is not directly observable. Thus, these latent variables
have to be estimated, e.g. with the help of a model.
<br> In the course of our discussion, we will encounter two major
modalities. The rst one is Ballistocardiography (BCG),
a modality often used in home monitoring applications, which
is based on simple pressure sensors, yielding a scalar signal.
The second modality is functional magnetic resonance imaging
(fMRI), a complex and highly sophisticated method, capable of
generating images of brain functionality.
<br> In the rst half of this thesis, we will focus on signal separation
and denoising methods for BCG. The performance of
these methods is then veried with model generated data, which
provides a very common example of how modelling interacts
with algorithm design. However, the relationship between algorithms
and modelling goes much deeper, since new insights
gained through better signal processing methods can also inspire
new models. This can be seen from the improved probabilistic
BCG model, which emerged from the results of the BCG signal
separation and denoising method. Finally, the new model opens
the possibility for probabilistic higher level analysis of the BCG
signal, which exemplies how improvements in modelling leads
to improved algorithms.
<br> In the latter half of the thesis, we will focus on embedded
clustering for fMRI data, which allows us to perform model inversion
and clustering at the same time. Here we see that although
there are great dierences between the two modalities BCG and
fMRI, the model based approach reveals how methods developed
for BCG can be applied to fMRI. This again demonstrates the
importance of a model based view on algorithm design.
urn:nbn:de:hbz:468-20150904-110417-0
2018-01-22T10:48:41.154Z
2018-01-22T12:09:34.519Z
published
Diss
fbc/physik/diss2015/yao