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      • Department of Electrical and Electronics Engineering
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      Akut koroner sendromun destek vektör makinelerine ve EKG’ye dayalı tespiti

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      Author(s)
      Terzi, Merve Begüm
      Arıkan, Orhan
      Date
      2019-04
      Source Title
      27th Signal Processing and Communications Applications Conference (SIU), 2019
      Publisher
      IEEE
      Pages
      1 - 4
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
      193
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      107
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      Abstract
      Akut koroner sendroma (AKS) sahip hastalarda, miyokard infarktüsü başlangıcından önce geçici göğüs ağrıları ile birlikte EKG sinyalinin ST segmentinde ve T dalgasında değişiklikler meydana gelmektedir. Bu çalışmada, AKS’nin gürbüz tespitini gerçekleştirmek amacıyla, EKG sinyalinin ST segmentindeki ve T dalgasındaki anomalileri güncel sinyal işleme ve makine öğrenmesi tekniklerini kullanarak tespit eden bir teknik geliştirilmiştir. Bu amaçla, STAFF III veri tabanındaki geniş bantlı kayıtlar kullanılarak, AKS’nin teşhisi için ayırıcılığı en yüksek olan EKG özniteliklerini elde eden özgün bir öznitelik çıkarım tekniği geliştirilmiştir. Elde edilen kritik öznitelikleri kullanarak, AKS’nin gürbüz tespitini gerçekleştiren destek vektör makinelerine (DVM) ve çekirdek fonksiyonlarına dayalı bir gözetimli öğrenme tekniği geliştirilmiştir. Önerilen tekniğin STAFF III veri tabanındaki kayda değer sayıda hastadan elde edilen başarım sonuçları, tekniğin oldukça güvenilir AKS tespiti sağladığını göstermektedir.
       
      In patients with acute coronary syndrome (ACS), transient chest pains together with changes in ST segment and T wave of ECG signal occur before the start of myocardial infarction. In this study, a technique which detects the anomalies in ST segment and T wave of ECG signal by using the state-of-theart signal processing and machine learning methods is developed to perform the robust detection of ACS. For this purpose, by using the wideband recordings on STAFF III database, a novel feature extraction technique which obtains the most discriminative ECG features for the detection of ACS is developed. By using the critical features, a supervised learning technique based on support vector machines (SVM) and kernel functions which performs the robust detection of ACS is developed. The performance results of the proposed technique obtained from a considerable number of patients on STAFF III database indicate that the technique provides highly reliable detection of ACS.
      Keywords
      Acute coronary syndrome
      Electrocardiogram
      Anomaly detection
      Feature extraction
      Support vector machines
      Kernel functions
      Permalink
      http://hdl.handle.net/11693/52958
      Published Version (Please cite this version)
      https://doi.org/10.1109/SIU.2019.8806272
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      • Department of Electrical and Electronics Engineering 3863
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