Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96259
Title: Novel classification of coronary artery disease using heart rate variability analysis
Authors: Dua, Sumeet
Du, Xian
Sree, Subbhuraam Vinitha
V.I., Thajudin Ahamed
Keywords: DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
Issue Date: 2012
Source: Dua, S., Du, X., Sree, S. V., & V. I., T. A. (2012). Novel classification of coronary artery disease using heart rate variability analysis. Journal of mechanics in medicine and biology, 12(04), 1240017-.
Series/Report no.: Journal of mechanics in medicine and biology
Abstract: Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced significantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended fluctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample entropy (SampEn). These features were subjected to principal component analysis (PCA). The significant principal components were evaluated using eight classification techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classification accuracy (89.5%) using our proposed technique.
URI: https://hdl.handle.net/10356/96259
http://hdl.handle.net/10220/11428
DOI: 10.1142/S0219519412400179
Rights: © 2013 World Scientific Publishing Co.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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