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

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.