An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals
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- 作者：Mohit Kumar ; a ; phd1401202005@iiti.ac.in" class="auth_mail" title="E-mail the corresponding author ; Ram Bilas Pachoria ; pachori@iiti.ac.in" class="auth_mail" title="E-mail the corresponding author ; U. Rajendra Acharyab ; aru@np.edu.sg" class="auth_mail" title="E-mail the corresponding author
- 关键词：HRV ; CAD ; FAWT ; Nonlinear features ; LS-SVM
- 刊名：Expert Systems with Applications
- 出版年：2016
- 出版时间：30 November 2016
- 年：2016
- 卷：63
- 期：Complete
- 页码：165-172
- 全文大小：852 K
文摘
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Classification of normal and CAD subjects is proposed using HRV signals.
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FAWT is used to decompose the HRV signal.
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K-NN entropy estimator and fuzzy entropy are used for feature extraction.
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Obtained classification accuracy of 100%.
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