Keywords

DSP, Signal Processing, MRI, ECG, MHD, MFD, simulation, model, MATLAB, LMS, Adaptive Filter, biomedical, aorta, induced potential

Abstract

This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2007

Semester

Fall

Advisor

Weeks, Arthur

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Science

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0001857

URL

http://purl.fcla.edu/fcla/etd/CFE0001857

Language

English

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Download

Share

COinS