Abstract
Extraction of useful information from cardiac signals for the diagnosis of diseases
and judgment of heart function is of special interest to medical personnel. Thus,
the development of effective, robust, and efficient diagnostic tools for heart diseases is
required. The aim when developing new techniques and tools is to minimize the
required cost and long hospitalization time, and increase the patient’s ease and safety. In
accordance with this statement, in this PhD thesis, non-invasive electrical-based
methods are of special interest. However, extracting useful information from measured
biomedical data is not always trivial. The research community, including our previous
contributions, has developed many algorithms for separating the signals of different
origins, e.g., cardiac, respiratory, and muscular activities, etc. Nevertheless, none of the
existing methods provides any mechanism to evaluate the performance of the developed
algorithms. Thus, there exist uncertainties regarding the properties of the signals, such
as its amplitude, waveform, components, and the origin of the signal waveform, which,
in turn, limits the quality of the diagnostics of diseases and conditions.
In this PhD thesis, it is argued that modelling the measured signals offers several
advantages to help dealing with the above problems, as compared to relying on
measured data only. By using a formalized representation, the parameters of the signal
model can be easily manipulated and/or modified, thus providing mechanisms that
allow researchers to reproduce and control such signals.
In turn, having such a formalized signal model makes it possible to develop
computer tools that can be used for manipulating and understanding how the signal
changes depend on various heart conditions, as well as for generating input signals for
experimenting with and evaluating the performance of, e.g. useful signal extraction
methods.
In this work, the focus is on bioelectrical information, mainly electrical bioimpedance
(EBI). Once the EBI is measured, it is necessary to model the corresponding
signals for analysis. In this case, the so-called advanced user should have to follow a
structured approach to move from real measured data to the model of the corresponding
signals. For this, a generic framework is proposed in the PhD work. It has been used to
guide the modelling of the impedance cardiography (ICG) and impedance respirography
(IRG) signals. Here, based on statistical parameters and visual fit, a Fourier series is
selected to model the ICG and IRG signals.
The proposed framework has been used to guide the development of the
corresponding bio-impedance signal simulator (BISS). The internal details of the
simulator are presented, including the various model parameters and the mechanisms for
adding modulation, noise, and motion artefacts. As a result, the implemented BISS
generates simulated EBI signals and BISS gives freedom to the end-user to control the
essential properties of the generated EBI signals depending on his/her needs. Predefined
human conditions/activities states are also included for ease of use.
and judgment of heart function is of special interest to medical personnel. Thus,
the development of effective, robust, and efficient diagnostic tools for heart diseases is
required. The aim when developing new techniques and tools is to minimize the
required cost and long hospitalization time, and increase the patient’s ease and safety. In
accordance with this statement, in this PhD thesis, non-invasive electrical-based
methods are of special interest. However, extracting useful information from measured
biomedical data is not always trivial. The research community, including our previous
contributions, has developed many algorithms for separating the signals of different
origins, e.g., cardiac, respiratory, and muscular activities, etc. Nevertheless, none of the
existing methods provides any mechanism to evaluate the performance of the developed
algorithms. Thus, there exist uncertainties regarding the properties of the signals, such
as its amplitude, waveform, components, and the origin of the signal waveform, which,
in turn, limits the quality of the diagnostics of diseases and conditions.
In this PhD thesis, it is argued that modelling the measured signals offers several
advantages to help dealing with the above problems, as compared to relying on
measured data only. By using a formalized representation, the parameters of the signal
model can be easily manipulated and/or modified, thus providing mechanisms that
allow researchers to reproduce and control such signals.
In turn, having such a formalized signal model makes it possible to develop
computer tools that can be used for manipulating and understanding how the signal
changes depend on various heart conditions, as well as for generating input signals for
experimenting with and evaluating the performance of, e.g. useful signal extraction
methods.
In this work, the focus is on bioelectrical information, mainly electrical bioimpedance
(EBI). Once the EBI is measured, it is necessary to model the corresponding
signals for analysis. In this case, the so-called advanced user should have to follow a
structured approach to move from real measured data to the model of the corresponding
signals. For this, a generic framework is proposed in the PhD work. It has been used to
guide the modelling of the impedance cardiography (ICG) and impedance respirography
(IRG) signals. Here, based on statistical parameters and visual fit, a Fourier series is
selected to model the ICG and IRG signals.
The proposed framework has been used to guide the development of the
corresponding bio-impedance signal simulator (BISS). The internal details of the
simulator are presented, including the various model parameters and the mechanisms for
adding modulation, noise, and motion artefacts. As a result, the implemented BISS
generates simulated EBI signals and BISS gives freedom to the end-user to control the
essential properties of the generated EBI signals depending on his/her needs. Predefined
human conditions/activities states are also included for ease of use.
| Original language | English |
|---|---|
| Qualification | Doctor of Philosophy |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 5 Jun 2015 |
| Publisher | |
| Electronic ISBNs | 9789949237630 |
| Publication status | Published - 5 Jun 2015 |