Abstract
Impedance cardiography (ICG) is a noninvasive method used to evaluate
several cardio dynamic parameters. ICG measures the dynamic transthoracic
electrical impedance by placing electrodes on the thorax to identify and
quantify conductivity changes inside the thorax during a cardiac cycle.
Since blood is much more conductive than other tissues and its electrical
conductivity varies with changes in the blood flow, it has an enormous
impact on the impedance changes measured by ICG. Pathological changes
in the aorta, like aortic dissection (AD), will alter the aortic shape and
blood flow, distorting the evaluated cardio dynamic parameters, but it could
potentially identify aortic pathology.
A 3D numerical simulation model based on the simplified geometry of
the human thorax has been set up to compute the impedance changes
on the thorax surface in the case of type B aortic dissection. Since the
blood conductivity variations play a significant role in obtaining diverging
impedance signals in a case with aortic pathology compared to a healthy
case, these variations during a cardiac cycle have been calculated so as to
distinguish between different health conditions in the simulation models.
A sensitivity analysis is applied using a set of simulation models with
different patient parameters to find the model parameters most sensitive to
differences in impedance signals in the dissected cases and investigate the
suitability of different electrode configurations considering several patientspecific
cases. The results confirm the hypothesis that the impact of aortic
pathology on the impedance signal is significant.
Bayesian probability theory is used to determine the probabilities for the
underlying physiological parameters in different health conditions, yielding
a classification of the healthy and dissected cases. For this purpose, the
recorded impedance signals from multiple sensors imposed by multiple injectors are combined. Then, the inverse problem is solved based on the
Bayesian probability theory. The results show that the proposed multisensors
ICG is more robust and has a better detection rate than conventional
ICG, helping clinicians draw more reliable conclusions in critical situations
in the medical management of aortic dissection.
False lumen thrombosis changes the exchange rate of the blood flow between
the false and true lumen, and thus, the blood flow induced conductivity
changes in different regions of a dissected aorta. Also, the thrombus growth
changes the spatial distribution of conductivity in the aortic region since
the conductivity of blood decreases significantly during thrombosis. For a
patientspecific case with known physiological parameters such as haematocrit
level, inlet flow condition and size of dissected anatomy, the thrombosis
in the false lumen and its impact on the blood flow induced conductivity
changes and the simulated impedance cardiogram is investigated. Results
show that using ICG could help monitor thrombosis in the false lumen in
patients with chronic type B aortic dissection.
several cardio dynamic parameters. ICG measures the dynamic transthoracic
electrical impedance by placing electrodes on the thorax to identify and
quantify conductivity changes inside the thorax during a cardiac cycle.
Since blood is much more conductive than other tissues and its electrical
conductivity varies with changes in the blood flow, it has an enormous
impact on the impedance changes measured by ICG. Pathological changes
in the aorta, like aortic dissection (AD), will alter the aortic shape and
blood flow, distorting the evaluated cardio dynamic parameters, but it could
potentially identify aortic pathology.
A 3D numerical simulation model based on the simplified geometry of
the human thorax has been set up to compute the impedance changes
on the thorax surface in the case of type B aortic dissection. Since the
blood conductivity variations play a significant role in obtaining diverging
impedance signals in a case with aortic pathology compared to a healthy
case, these variations during a cardiac cycle have been calculated so as to
distinguish between different health conditions in the simulation models.
A sensitivity analysis is applied using a set of simulation models with
different patient parameters to find the model parameters most sensitive to
differences in impedance signals in the dissected cases and investigate the
suitability of different electrode configurations considering several patientspecific
cases. The results confirm the hypothesis that the impact of aortic
pathology on the impedance signal is significant.
Bayesian probability theory is used to determine the probabilities for the
underlying physiological parameters in different health conditions, yielding
a classification of the healthy and dissected cases. For this purpose, the
recorded impedance signals from multiple sensors imposed by multiple injectors are combined. Then, the inverse problem is solved based on the
Bayesian probability theory. The results show that the proposed multisensors
ICG is more robust and has a better detection rate than conventional
ICG, helping clinicians draw more reliable conclusions in critical situations
in the medical management of aortic dissection.
False lumen thrombosis changes the exchange rate of the blood flow between
the false and true lumen, and thus, the blood flow induced conductivity
changes in different regions of a dissected aorta. Also, the thrombus growth
changes the spatial distribution of conductivity in the aortic region since
the conductivity of blood decreases significantly during thrombosis. For a
patientspecific case with known physiological parameters such as haematocrit
level, inlet flow condition and size of dissected anatomy, the thrombosis
in the false lumen and its impact on the blood flow induced conductivity
changes and the simulated impedance cardiogram is investigated. Results
show that using ICG could help monitor thrombosis in the false lumen in
patients with chronic type B aortic dissection.
Translated title of the contribution  Modellierung und Simulation Thorakaler Bioimpedanzänderungen zur Identifizierung von Aortendissektionen 

Original language  English 
Qualification  Doctor of Technology 
Awarding Institution 

Supervisors/Advisors 

Award date  23 Sept 2021 
Publication status  Published  2021 
Keywords
 Impedance cardiography
 Aortic dissection
 Numerical Simulation
 Thrombosis