The study of spatiotemporal scaling features and correlations in complex biomedical data

Abstract

In this research, we demonstrate the capabilities of the normalized range method (R/S analysis) in the study of fractal patterns in biomedical data of complex living systems.The Hurst exponent allows differentiating temporal signals in the presence of minimal information about the complex system under study, depending on the nature of the correlations manifestation. The capabilities of the proposed algorithms were demonstrated by analyzing the scaling features of the temporal dynamics of the tremor rate in Parkinson's disease, the bioelectrical activity of the brain of patients with epilepsy, including those under external influences. The results can be used in computational biophysics and physics of complex systems to search for diagnostic criteria for neurological and neurodegenerative diseases, as well as to study the processes of biological aging and changes in the “physiological complexity” of the human body.