In this thesis, a methodology for the detection of anomalies in the cardiovascular
system is presented. The cardiovascular system is one of the most fascinating and
complex physiological systems. Nowadays, cardiovascular diseases constitute one of
the most important causes of mortality in the world. For instance, an estimate of 17.3
million people died in 2008 from cardiovascular diseases. Therefore, many studies have
been devoted to modeling the cardiovascular system in order to better understand its
behavior and find new reliable diagnosis techniques.
The lumped parameter model of the cardiovascular system proposed in [1] is restructured
using a hybrid systems approach in order to include a discrete input vector
that represents the influence of the mitral and aortic valves in the different phases of
the cardiac cycle. Parting from this model, a Taylor expansion around the nominal
values of a vector of parameters is conducted. This expansion serves as the foundation
for a component fault detection process to detect changes in the physiological
parameters of the cardiovascular system which could be associated with cardiovascular
anomalies such as atherosclerosis, aneurysm, high blood pressure, etc. An Extended Kalman Filter is used in order to achieve a joint estimation of the state vector and
the changes in the considered parameters. Finally, a bank of filters is, as in [2], used
in order to detect the appearance of heart valve diseases, particularly stenosis and
regurgitation. The first numerical results obtained are presented.
| Date of Award | Jul 2012 |
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| Original language | English (US) |
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| Awarding Institution | - Physical Sciences and Engineering
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| Supervisor | Meriem Laleg (Supervisor) |
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- Observers
- Hybrid system
- Fault detection and isolation
- Cardiovascular system