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|Title:||Automated diagnosis of cardiac health using recurrence quantification analysis||Authors:||Ng, Kwan-Hoong
Krishnan, M. Muthu Rama
Sree, Subbhuraam Vinitha
Ng, Eddie Yin-Kwee
Ghista, Dhanjoo N.
Ang, Alvin P. C.
Suri, Jasjit S.
|Keywords:||DRNTU::Engineering::Mechanical engineering::Bio-mechatronics||Issue Date:||2012||Source:||Krishnan, M. M. R., Sree, S. V., Ghista, D. N., Ng, E. Y. K., Swapna, Ang, A. P. C., et al. (2012). Automated diagnosis of cardiac health using recurrence quantification analysis. Journal of mechanics in medicine and biology, 12(04), 1240014-.||Series/Report no.:||Journal of mechanics in medicine and biology||Abstract:||The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets.||URI:||https://hdl.handle.net/10356/96466
|DOI:||http://dx.doi.org/10.1142/S0219519412400143||Rights:||© 2012 World Scientific Publishing Company||metadata.item.grantfulltext:||none||metadata.item.fulltext:||No Fulltext|
|Appears in Collections:||MAE Journal Articles|
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