Nonlinear analysis or coronary artery disease, myocardial infarction, and normal ECG signals

HAGIWARA, Yuki and FAUST, Oliver (2017). Nonlinear analysis or coronary artery disease, myocardial infarction, and normal ECG signals. Journal of Mechanics in Medicine and Biology, 17 (7), p. 1740006.

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Official URL: http://www.worldscientific.com/doi/abs/10.1142/S02...
Link to published version:: 10.1142/s0219519417400061

Abstract

In this study, we analyze nonlinear feature extraction methods in terms of their ability to support the diagnosis of coronary artery disease (CAD) and myocardial infarction (MI). The nonlinear features were extracted from electrocardiogram (ECG) signals that were measured from CAD patients, MI patients as well as normal controls. We tested 34 recurrence quantification analysis (RQA) features, 14 bispectrum, and 136 cumulant features. The features were extracted from 10,546 normal, 41,545 CAD, and 40,182 MI heart beats. The feature quality was assessed with Student’s tt-test and the tt-value was used for feature ranking. We found that nonlinear features can effectively represent the physiological realities of the human heart.

Item Type: Article
Additional Information: ** From Crossref via Jisc Publications Router.
Uncontrolled Keywords: Biomedical Engineering
Departments: Arts, Computing, Engineering and Sciences > Engineering and Mathematics
Identification Number: 10.1142/s0219519417400061
Related URLs:
SWORD Depositor: Hilary Ridgway
Depositing User: Hilary Ridgway
Date Deposited: 20 Oct 2017 08:34
Last Modified: 20 Oct 2017 08:34
URI: http://shura.shu.ac.uk/id/eprint/17083

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