An Evolution of Our Understanding of Decomplexification Estimation for Early Detection, Monitoring and Modeling of Human Physiology

Human physiology is among the most complex systems in nature, characterized by intricate structural and functional networks and rich temporal dynamics. Electrophysiological signals produced by different tissues/organs reflect physiological activity, and are inherently non-stationary, non-linear, and noisy. This work focuses on fractal analysis, a framework that captures the self-similar and scale-free properties of electrophysiological signals, which is considered to act as an output of complex physiological structures that generate complex processes. Central to this approach is the principle of ‘decomplexification’, whereby aging and disease are associated with a loss of physiological complexity. We discuss key algorithms, particularly Higuchi’s fractal dimension, which is often combined with other nonlinear measures and machine-learning models for real-time analysis of electrophysiological signals. Evidence shows that fractal metrics enable the early detection and monitoring of neurological and psychiatric disorders, outperforming traditional spectral measures. In movement disorders and mood disorders, fractal and nonlinear features show high diagnostic accuracy. Beyond diagnostics, we discuss therapeutic applications, including the prediction of responsiveness to non-invasive brain stimulation. Here, we envisage the evolution of one fractal or nonlinear measure use, to several measures applied, then use it as a feature for machine learning, and then realize that a whole cluster of biomarkers must be used to reflect the state of autonomic profile, which then can be used for ontology-based application profiles that can be machine-actionable. In addition, we discuss the fractal and fractional description of transport processes, which offer innovative improvement for a much more accurate description of physiological reality as a prerequisite for further modeling: for example, this is needed for digital twins to support the clinical translation of fractal analysis for personalized medicine. In essence, if one is trying to mathematically describe or quantify structures or processes in human physiology, fractal and fractional are the supreme and adequate approach to accurately model that reality.