Terzi, Merve Begüm2016-01-082016-01-082014http://hdl.handle.net/11693/18336Ankara : The Department of Electrical and Electronics Engineering and The Graduate School of Engineering and Science of Bilkent Univesity, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 79-85.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.xvi, 85 leaves, illustrations, graphicsEnglishinfo:eu-repo/semantics/openAccessElectrocardiogram (ECG) signal classificationFeature extractionSupport Vector Machine (SVM)Kernel MethodAcute Coronary SyndromeAcute Myocardial InfarctionWG300 .T47 2014Electrocardiography.Coronary heart disease.Human-computer interaction.ThesisB148323