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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 | Schools: | School of Mechanical and Aerospace Engineering | Rights: | © 2013 World Scientific Publishing Co. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | MAE Journal Articles |
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