An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals

作者：

Highlights：

• Classification of normal and CAD subjects is proposed using HRV signals.

• FAWT is used to decompose the HRV signal.

• K-NN entropy estimator and fuzzy entropy are used for feature extraction.

• Obtained classification accuracy of 100%.

摘要

•Classification of normal and CAD subjects is proposed using HRV signals.•FAWT is used to decompose the HRV signal.•K-NN entropy estimator and fuzzy entropy are used for feature extraction.•Obtained classification accuracy of 100%.

论文关键词：HRV,CAD,FAWT,Nonlinear features,LS-SVM

论文评审过程：Received 4 December 2015, Revised 17 May 2016, Accepted 16 June 2016, Available online 27 June 2016, Version of Record 7 July 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.06.038