Automated Diagnosis of Coronary Artery Disease: A Review and Workflow. (4th February 2018)

• Record Type: Journal Article
• Authors: Mastoi, Qurat-ul-ain; Wah, Teh Ying; Gopal Raj, Ram; Iqbal, Uzair
• Other Names: von Haehling Stephan Academic Editor.
• Is Part Of: Cardiology research and practice. Volume 2018(2018)
• Publication Date: 2018-02-04
• Subjects: Cardiology -- Research -- Periodicals; Cardiology -- Periodicals; Cardiology; Cardiology; Cardiology -- Research; Electronic journals; Periodicals; Periodicals; 616.12
• DOI: 10.1155/2018/2016282
• Languages: English
• ISSNs: 2090-8016

Abstract

Abstract : Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.