Abstract
Ischemic heart disease is the single most frequent cause of death in
the world today. Electrocardiogram (ECG) exercise testing is a common
tool for diagnosing this disease. With the ECG exercise test one can
compare the recording of the electrical activity in the heart during
exercise and rest. A shift between the rest and exercise recording in
the segment of the heartbeat called the ST segment is used both for
diagnosing ischemia, and as input to cardiac computation methods.
Body surface potential mappings (BSPM) are ECG recordings at a greater
number of locations on the torso, and provides better detection and
localisation properties than the traditional 12-lead ECG. In BSPM and ECG,
noise and drift from various sources are recorded in addition to the
signal propagating from the heart. A model for this is:
BSPM=signal+noise+drift.
Before accurate measurements of the ST segments in a BSPM
can be made, the noise and drift in the recording must be reduced
while keeping the signal unchanged. In this thesis, an automatic
algorithm for post processing raw BSPM data recordings was made.
The following methods have
been developed, implemented and tested as part of the algorithm:
First, noise reduction methods using frequency based filtering
techniques was implemented and tested. Second, an algorithm for
detecting the BSPM signal peaks was developed. This method was used to
locate the interesting parts of each heartbeat.
Third, methods for removing
the baseline drift is discussed. Four methods were selected,
implemented and evaluated against each other. A method using cubic
spline interpolation as an approximation to the drift was deemed
best and used in the automatic algorithm. Even after this initial
processing, there may be noisy or corrupted signal parts present in a
BSPM. Hence a framework for removing such parts of the BSPM was
developed as the fourth step of the algorithm. In the fifth step, a
robust method for computing the ST segment shifts at each electrode
location from a processed
BSPM was made. Finally, a tool for visualising these shifts was
created.
The algorithm developed in this thesis was applied to BSPM recordings
of real patients. Before processing, it was not possible to compute
neither reliable nor correct ST shifts from these recordings. After
the automatic algorithm was applied to these recordings, all the
resulting BSPMs were physically realistic, and showed signs of being
close to the true values. The computed ST shifts from the processed
data showed promising results for diagnosing ischemia in the limited
set of BSPMs available. In addition, a comparison between traditional
ECG and BSPM was made.