Multiscale ordinal network analysis of human cardiac dynamics

M. McCullough, M. Small, H. H.C. Iu, T. Stemler

    Research output: Contribution to journalArticle

    18 Citations (Scopus)
    429 Downloads (Pure)

    Abstract

    In this study, we propose a new information theoretic measure to quantify the complexity of biological systems based on time-series data. We demonstrate the potential of our method using two distinct applications to human cardiac dynamics. Firstly, we show that the method clearly discriminates between segments of electrocardiogram records characterized by normal sinus rhythm, ventricular tachycardia and ventricular fibrillation. Secondly, we investigate the multiscale complexity of cardiac dynamics with respect to age in healthy individuals using interbeat interval time series and compare our findings with a previous study which established a link between age and fractal-like long-range correlations. The method we use is an extension of the symbolic mapping procedure originally proposed for permutation entropy. We build a Markov chain of the dynamics based on order patterns in the time series which we call an ordinal network, and from this model compute an intuitive entropic measure of transitional complexity. A discussion of the model parameter space in terms of traditional time delay embedding provides a theoretical basis for our multiscale approach. As an ancillary discussion, we address the practical issue of node aliasing and how this effects ordinal network models of continuous systems from discrete time sampled data, such as interbeat interval time series. This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

    Original languageEnglish
    Article number20160292
    JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
    Volume375
    Issue number2096
    DOIs
    Publication statusPublished - 28 Jun 2017

    Fingerprint Dive into the research topics of 'Multiscale ordinal network analysis of human cardiac dynamics'. Together they form a unique fingerprint.

  • Projects

    Cite this