Publication Type : Journal Article
Publisher : IRBM, Elsevier
Source : IRBM, Elsevier, In Press, Corrected Proof, Available online 25 June 2020.
Url : https://doi.org/10.1016/j.irbm.2020.06.004 (Impact Factor: 1.856)
Campus : Chennai
School : School of Engineering
Center : Amrita Innovation & Research
Department : Electronics and Communication
Verified : Yes
Year : 2020
Abstract : This study focuses on integration of anatomical left ventricle myocardium features and optimized extreme learning machine (ELM) for discrimination of subjects with normal, mild, moderate and severe abnormal ejection fraction (EF). The physiological alterations in myocardium have diagnostic relevance to the etiology of cardiovascular diseases (CVD) with reduced EF.
Cite this Research Publication : MuthulakshmiMuthunayagam, KavithaGanesan, “Cardiovascular Disorder Severity Detection Using Myocardial Anatomic Features Based Optimized Extreme Learning Machine Approach”, IRBM, Elsevier, In Press, Corrected Proof, Available online 25 June 2020. https://doi.org/10.1016/j.irbm.2020.06.004 (Impact Factor: 1.856)