[1]YANG Xiang-lin,YAN Hong,REN Zhao-rui,et al.A method based on the PCA feature and fusion feature for ECG human identification[J].CAAI Transactions on Intelligent Systems,2010,5(5):458-463.[doi:10.3969/j.issn.1673-4785.2010.05.014]
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A method based on the PCA feature and fusion feature for ECG human identification

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 5 Page number: 458-463 Column: 学术论文—机器感知与模式识别 Public date: 2010-10-25
Title:
A method based on the PCA feature and fusion feature for ECG human identification
Author(s):
YANG Xiang-lin;  YAN Hong;  REN Zhao-rui;  SONG Jin-zhong;  YAO Yu-hua;  LI Yan-jun
China Astronaut Research and Training Center, Beijing 100193, China
Keywords:
principal component analysis(PCA);  wavelet decomposition;  fusion feature;  electrocardiogram(ECG);  identification
CLC:
TP301.6
DOI:
10.3969/j.issn.1673-4785.2010.05.014
Abstract:
As a new biometric for identification, ECG attracted widespread attention in the international community. The method was based on the analytic feature for ECG identification and required high precision for fiducial points detection. To overcome this disadvantage, a method, in which only the peak point of the R-wave detection is needed, was proposed. As for setting the relevant threshold, this method combined the PCA feature method and fusion feature method based on wavelet decomposition. The experiment demonstrates that the method proposed in this paper is better than the PCA feature method, waveform feature method, and wavelet feature method. This method, which not only reduces the complexity and error of the fiducial points detection but also achieves high accuracy, is a realistic and efficient algorithm.
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