[A rough set method to treat ECG signals for predicting coronary heart disease].
In this paper is used the rough set theory to deal with the information contained in electrocardiographic waveforms and to find out the correlation between coronary heart disease (CHD) and the selected indices in electrocardiographic lead I. The principles of attribute reduction are applied, the redundancy of a decision-making table is reduced, and the important characters and diagnostic rules are extracted. The real case analysis shows that clear and concise diagnostic rules can be established by using rough set theory, which can be helpful to the clinical diagnosis of CHD.
Gao
Xiaokang
X
School of Mechanical and Automation Engineering, Shanghai Institute of Technology, Shanghai 200233, China. szwgxk@online.sh.cn
Zhao
Rongyong
R
Qi
Congqian
C
Shi
Zhongwei
Z
D000465:Algorithms; D003327:Coronary Disease / Q000175:diagnosis*; D003658:Decision Making, Computer-Assisted; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted