Browsing by Subject "Electrocardiogram (ECG) signal classification"

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    Akut koroner sendromların otomatik ST/T sınıflandırıcısı ile erken tanısı
    (IEEE, 2014-10) Terzi, M. Begüm; Arıkan, Orhan; Abacı, A.; Candemir, M.; Dedoğlu, Mehmet
    In patients with acute coronary syndrome, temporary chest pains together with changes in ECG ST segment and T wave occur shortly before the start of myocardial infarction. In order to diagnose acute coronary syndromes early, a new technique which detects changes in ECG ST/T sections is developed. As a result of implementing the developed technique to real ECG recordings, it is shown that the proposed technique provides reliable detections. Therefore, the developed technique is expected to provide early diagnosis of acute coronary syndromes which will lead to a significant decrease in heart failure and mortality rates. © 2014 IEEE.
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    Early diagnosis of acute coronary syndromes automatically by using features of ECG recordings = EKG kayıtlarının öznitelikleri kullanılarak akut koroner sendromların otomatik olarak erken teşhisi
    (2014) Terzi, Merve Begüm
    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.