Early diagnosis of acute coronary syndromes automatically by using features of ECG recordings = EKG kayıtlarının öznitelikleri kullanılarak akut koroner sendromların otomatik olarak erken teşhisi
Author(s)
Advisor
Arıkan, OrhanDate
2014Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
In patients with acute coronary syndrome, temporary chest pains together
with changes in the ST/T sections of ECG occur shortly before the start of
myocardial infarction. In order to diagnose acute coronary syndromes early, we
propose a new technique which detects changes in the ST/T sections of ECG. For
this purpose, by using real ECG recordings, we identify ECG features that are
critical in the detection of acute coronary syndromes. By using support vector
machines (SVM) operating with linear and radial basis function (RBF) kernels,
we obtain classifiers that use 2 or 3 most discriminating features of the ST/T
sections. To improve performance, classification results on multiple segments are
fused. The obtained results over a considerable number of patients indicate that
the proposed classification technique provides highly reliable detection of acute
coronary syndromes. To develop a detection technique that can be used in the
absence of unhealthy ECGs, we also investigate the detection of acute coronary
syndromes based on ECG recordings of a patient obtained during healthy stage
only. For this purpose, a Gaussian mixture model is used to represent the joint
pdf of the selected features. Then, a Neyman-Pearson type of approach is developed
to provide detection of outliers that would correspond to acute coronary
syndromes.
Keywords
Electrocardiogram (ECG) signal classificationFeature extraction
Support Vector Machine (SVM)
Kernel Method
Acute Coronary Syndrome
Acute Myocardial Infarction