Effects of controlling parameter on symbolic nonlinear complexity detection

TL;DR

This paper investigates how the controlling parameter in the KW symbolic method affects the detection of nonlinear complexity in various systems, including chaotic models and heart rate data, highlighting the importance of parameter adjustment for accurate analysis.

Contribution

It demonstrates the impact of the KW symbolic method's controlling parameter on nonlinear complexity detection across different systems, emphasizing the need for parameter tuning.

Findings

Parameter adjustment improves reliability of nonlinear analysis.

Complexity loss correlates with aging and heart disease.

Different systems require different parameter settings.

Abstract

Symbolic transformation, a coarse-graining process, is a crucial prerequisite for and has evidential influence to the symbolic time series analysis. We employ Shannon entropy for a parameter-dependent symbolization, KW (Kurths-Wessel) symbolic method, to test the effects of controlling parameter on its symbolic nonlinear complexity detection. Two chaotic models, logistic and Henon series, and heartbeats of CHF (Congestive Heart Failure) patients, healthy young and elderly subjects from PhysioNet are applied to test the KW symbolic entropy. The complexity-loss theory about aging and diseases in heart rates is validated and reasons that may account for some paradoxes in nonlinear analysis are discussed. Tests results suggest that due to different structural or dynamical properties of different nonlinear systems, controlling parameter of the KW symbolization should be adjusted accordingly to have reliable symbolic nonlinear analysis.