NONLINEAR TIME SERIES ANALYSIS USING ORDINAL NETWORKS WITH SELECT APPLICATIONS IN BIOMEDICAL SIGNAL PROCESSING
Michael McCullough · Bulletin of the Australian Mathematical Society · 2019
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.