Research of complex physiological signals based on nonlinear theory

Life is one of the most complicated nonlinear systems. Thus, the nonlinear analysis methods can be better to disclose its characteristics and mechanisms. In this paper, we introduce a new measure to characterize multifractality, the mass exponent spectrum curvature, which can disclose the complexity of fractal structure from the total bending degree of the spectrum. The evaluations of Cantor measure validate it is entirely effective in exploring the complexity of chaotic series, and also has stronger ability to resist disturbances. We then apply this method to the analyses of human heart rate variability signals and sleeping electroencephalogram signals. The experimental results show this method can be better to discriminate the cohorts under different physiological and pathological status. These conclusions can be useful in early diagnoses and clinical applications.