01587nas a2200205   4500000000100000000000100001008004100002260001200043653001900055653001000074653000800084100001500092700001400107245011200121856009900233300001000332490000600342520101900348022001401367       2020                            d  c03/202010aClassification10aFuzzy10aECG1 aB S Harish1 aC K Roopa00aAutomated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/02/ijimai20206_1_2_pdf_13999.pdf  a16-250 v63 aCardio-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.  a1989-1660