Measurement of the complexity for low-dimensional, non-linear structure of respiratory network in human
低维非线性呼吸系统的复杂性计算

The nonlinear dynamical characteristics of respiratory variables recorded from male subjects during rest were analyzed. Three fundamental techniques were employed: correlation dimension D2 and the largest Lyapunov exponent LLE calculations as well as the surrogate data analysis. Furthermore, a novel approach named C0 complexity was introduced, which may improve the understanding of the underlying physiological processes of the autonomic/automatic nervous systems. The results suggest that although the pattern of breathing in the resting human might have properties consistent with that of a chaotic system, the evidence is not conclusive because the LLE values in original data do not differ from the LLE values in the surrogate data. However, the data suggest that the values of C0 complexity of several respiratory variables are significant. The results also suggest that many aspects of particularly breathing may show a non-random complex nature. Moreover, this method may allow us to quantify changes in the complexity of respiratory variables in response to challenges in a novel manner.