Nonlinear time series analysis using ordinal networks with select applications in biomedical signal processing

Michael Hugh McCullough

    Research output: ThesisDoctoral Thesis

    431 Downloads (Pure)

    Abstract

    This study defines and investigates ordinal networks as a new method for nonlinear time series analysis. An ordinal network is a Markov model of a time series that is constructed by applying an ordinal partition to a delay embedding. Numerical investigations show that the topology of an ordinal network can be measured to quantify dynamical complexity and nonlinear phenomena in discrete-time sampled date from archetypal continuous chaotic systems. These finding are developed into a framework and applied to study age-related effects and multi-scale complexity in cardiac dynamics, and to investigate the spatio-temporal dynamics of epileptic seizure onset.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • The University of Western Australia
    Supervisors/Advisors
    • Small, Michael, Supervisor
    • Stemler, Thomas, Supervisor
    • Iu, Ho Ching, Supervisor
    Thesis sponsors
    Award date25 Jan 2018
    DOIs
    Publication statusUnpublished - 2018

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