@article{2709,
  keywords = {Classification, Fuzzy, ECG},
  author = {B S Harish and C K Roopa},
  title = {Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network},
  abstract = {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.},
  year = {2020},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {6},
  number = {1},
  pages = {16-25},
  month = {03/2020},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/files/2019/02/ijimai20206_1_2_pdf_13999.pdf},
  doi = {10.9781/ijimai.2019.02.001},
}