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    • 02) Bitlis Eren University Journal of Science and Technology
    • Cilt 07, Sayı 2 (2017)
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    • 2-DERGİLER
    • 02) Bitlis Eren University Journal of Science and Technology
    • Cilt 07, Sayı 2 (2017)
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    Diagnostic model for identification of myocardial infarction from electrocardiography signals

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    Date
    2017
    Author
    DİKER, Aykut
    CÖMERT, Zafer
    AVCI, Engin
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    Abstract
    Electrocardiography (ECG) is a useful test used commonly to observe the electrical activity of a heart. Recently, a growing relationship has been observed between diagnosis of any heart disease and using of machine learning techniques. In this scope, a diagnostic application model designed based on a combination of Recursive Feature Eliminator (RFE) and two different machine learning algorithms called 𝑘-nearest neighbors (𝑘-NN) and artificial neural network (ANN) is proposed for classification of ECG signals in this study. The experiments performed on an open-access ECG database. Firstly, the signals were passed a pre-processing step. Then, several diagnostic features from morphological and statistical domains were extracted from ECG signals. In the last stage of the analysis, RFE algorithm covering 10- fold cross-validation and the mentioned machine learning techniques were employed to separate Myocardial Infarction (MI) samples from normal. The promising results as accuracy of 80.60%, sensitivity of 86.58% and specificity of 64.71% were achieved. The validation of the contribution was checked by comparing the performances of both 𝑘-NN and ANN to related works. Consequently, the proposed diagnostic model ensured an automatic and robust ECG signal classification model.
    URI
    http://dspace.beu.edu.tr:8080/xmlui/handle/123456789/13834
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    • Cilt 07, Sayı 2 (2017) [11]





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