Abstract:
Copyright © 2020 for this paper by its authors. The use of data science in the analysis of biomedical and physiological time series and spatial maps allows extracting reliable information about the dynamic states and functioning of the organism as a whole and of individual organs. In this paper, based on the Memory Function Formalism, one of the approaches of statistical physics, we analyze the signals of bioelectric activity of the human brain and the human neuromuscular system. We perform transition from the study of global patterns revealed in human signals to the analysis of individual sections of time dynamics. Based on localized characteristics and parameters (time window plotting of power spectra and statistical memory measure), we establish changes in periodic patterns and correlations of dynamic modes. In the case of time series analysis, various localization procedures play the role of a "statistical microscope"that captures signal details or reflects the features of the local structure of an object. Generalized and localized parameters introduced within the framework of the Memory Function Formalism prove to be useful in searching for diagnostic criteria in cardiology, neurophysiology, epidemiology, and in studying the human sensorimotor and locomotor activity.
