The Application of Two Dimensional Principal Component Analysis in the Detection of Myocardial Infarction ECG Signals
GE Ding-fei1, XU Ai-qun2
1. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang 310012, China;
2. School of Mechanical and Automotive Engineering,Zhejiang University of Science and Technology, Hangzhou, Zhejiang 310012, China
Abstract The joint energy percentage(EP) search method based on two dimensional principal component analysis is introduced to extract global features from 12-lead high resolution electrocardiogram(ECG) for the purpose of classification. Four types of classes are collected from PTB clinical diagnostic database, which corresponding patient’s statuses are health control, myocardial infarction(MI) in early stage, MI in acute stage and MI in recover stage, respectively. The experimental results show that the information can be fused efficiently using the proposed method, which are from 12-lead ECG and the details contained in high frequency components of ECG. The average classification accuracy can be increased by 10.43% compared with that of conventional principal component analysis. The ECG signal can be represented with lower dimensions compared with that of independent EP criterion, and an average classification accuracy of 99.46% can be achieved.
GE Ding-fei,XU Ai-qun. The Application of Two Dimensional Principal Component Analysis in the Detection of Myocardial Infarction ECG Signals[J]. Acta Metrologica Sinica, 2014, 35(3): 252-257.
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2014, Vol. 35 
