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

Michael Hugh McCullough

    Research output: ThesisDoctoral Thesis

    222 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

    Fingerprint

    Time series analysis
    Chaotic systems
    Time series
    Topology
    Biomedical signal processing

    Cite this

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    title = "Nonlinear time series analysis using ordinal networks with select applications in biomedical signal processing",
    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.",
    keywords = "nonlinear time series analysis, dynamical systems, Symbolic dynamics, Bioinformatics, Complex networks, Chaos, Ordinal patterns, complexity",
    author = "McCullough, {Michael Hugh}",
    year = "2018",
    doi = "10.4225/23/5a7a9572abc5f",
    language = "English",
    school = "The University of Western Australia",

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    Nonlinear time series analysis using ordinal networks with select applications in biomedical signal processing. / McCullough, Michael Hugh.

    2018.

    Research output: ThesisDoctoral Thesis

    TY - THES

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    AU - McCullough, Michael Hugh

    PY - 2018

    Y1 - 2018

    N2 - 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.

    AB - 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.

    KW - nonlinear time series analysis

    KW - dynamical systems

    KW - Symbolic dynamics

    KW - Bioinformatics

    KW - Complex networks

    KW - Chaos

    KW - Ordinal patterns

    KW - complexity

    U2 - 10.4225/23/5a7a9572abc5f

    DO - 10.4225/23/5a7a9572abc5f

    M3 - Doctoral Thesis

    ER -