Loading [a11y]/accessibility-menu.js
Order Statistics Concordance Coefficient With Applications to Multichannel Biosignal Analysis | IEEE Journals & Magazine | IEEE Xplore

Order Statistics Concordance Coefficient With Applications to Multichannel Biosignal Analysis


Abstract:

In this paper, we propose a novel concordance coefficient, called order statistics concordance coefficient (OSCOC), to quantify the association among multichannel biosign...Show More

Abstract:

In this paper, we propose a novel concordance coefficient, called order statistics concordance coefficient (OSCOC), to quantify the association among multichannel biosignals. To uncover its properties, we compare OSCOC with three other similar indexes, i.e., average Pearson's product moment correlation coefficient (APPMCC), Kendall's concordance coefficients (KCC), and average Kendall's tau (AKT), under a multivariate normal model (MNM), linear model (LM), and nonlinear model. To further demonstrate its usefulness, we present an example on atrial arrhythmia analysis based on real-world multichannel cardiac signals. Theoretical derivations as well as numerical results suggest that 1) under MNM and LM, OSCOC performs equally well with APPMCC, and outperforms the other two methods, 2) in nonlinear case, OSCOC even has better performance than KCC and AKT, which are well known to be robust under increasing nonlinear transformations, and 3) OSCOC performs the best in the case study of arrhythmia analysis in terms of the volume under the surface.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21, Issue: 5, September 2017)
Page(s): 1206 - 1215
Date of Publication: 11 October 2016

ISSN Information:

PubMed ID: 27740502

Funding Agency:


References

References is not available for this document.