Novel classification of coronary artery disease using heart rate variability analysis
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,...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/96259 http://hdl.handle.net/10220/11428 |
| _version_ | 1826112407381999616 |
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| author | Dua, Sumeet Du, Xian Sree, Subbhuraam Vinitha V.I., Thajudin Ahamed |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Dua, Sumeet Du, Xian Sree, Subbhuraam Vinitha V.I., Thajudin Ahamed |
| author_sort | Dua, Sumeet |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:06:30Z |
| format | Journal Article |
| id | ntu-10356/96259 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:06:30Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/962592020-03-07T13:22:14Z Novel classification of coronary artery disease using heart rate variability analysis Dua, Sumeet Du, Xian Sree, Subbhuraam Vinitha V.I., Thajudin Ahamed School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Bio-mechatronics 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. 2013-07-15T06:45:42Z 2019-12-06T19:27:56Z 2013-07-15T06:45:42Z 2019-12-06T19:27:56Z 2012 2012 Journal Article 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-. https://hdl.handle.net/10356/96259 http://hdl.handle.net/10220/11428 10.1142/S0219519412400179 en Journal of mechanics in medicine and biology © 2013 World Scientific Publishing Co. |
| spellingShingle | DRNTU::Engineering::Mechanical engineering::Bio-mechatronics Dua, Sumeet Du, Xian Sree, Subbhuraam Vinitha V.I., Thajudin Ahamed Novel classification of coronary artery disease using heart rate variability analysis |
| title | Novel classification of coronary artery disease using heart rate variability analysis |
| title_full | Novel classification of coronary artery disease using heart rate variability analysis |
| title_fullStr | Novel classification of coronary artery disease using heart rate variability analysis |
| title_full_unstemmed | Novel classification of coronary artery disease using heart rate variability analysis |
| title_short | Novel classification of coronary artery disease using heart rate variability analysis |
| title_sort | novel classification of coronary artery disease using heart rate variability analysis |
| topic | DRNTU::Engineering::Mechanical engineering::Bio-mechatronics |
| url | https://hdl.handle.net/10356/96259 http://hdl.handle.net/10220/11428 |
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