Chaos

Abstract

The theory of chaos was developed approximately 30 years ago and initially applied in meteorology. Chaotic systems are characterized by nonlinear representations whose long‐term behavior is sensitive to initial conditions. Although the results may appear random, the systems are in fact deterministic and repeatable if all conditions remain the same. Chaos theory has been applied to the analysis of biomedical time series, especially in electrocardiogram analysis for examination of cardiac disorders. Chaotic analysis is based on the use of one or more measures of chaos that include both graphical and numerical representations. Most chaotic models are discrete in nature. Examination of continuous models shows that some chaotic behavior is from the discrete representation, and the methods derived from continuous modeling can be used in both chaotic and nonchaotic situations. Chaos, nonlinear dynamics, and complex systems have become linked and have expanded into new areas of application in biomedicine, including examination of interactions of multiple components from the cellular to the body system level.

Phase space reconstruction and estimation of the largest Lyapunov exponent for gait kinematic data

“…The chaotic characteristics of the ECG signals (Lyapunov exponents spectrum and correlation dimension) were incorporated to the set of features for the purpose of biometric individual identification [2] but, first of all, chaos theory has been applied to the analysis of electrocardiogram for examination of cardiac disorders [3].…”