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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 1, march 2020
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 1, march 2020
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    Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

    Autor: 
    Harish, B S
    ;
    Roopa, C K
    Fecha: 
    03/2020
    Palabra clave: 
    classification; fuzzy; ECG; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12687
    DOI: 
    https://doi.org/10.9781/ijimai.2019.02.001
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2709
    Open Access
    Resumen:
    Cardio-vascular diseases are one of the foremost causes of mortality in today’s world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRST-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus.
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    Nombre: ijimai20206_1_2_pdf_13999.pdf
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