Principles Entailed by Complexity, Crucial Events, and Multifractal Dimensionality

Area of Science:

• Physiology
• Complexity Science
• Biomedical Engineering

Background:

• Complexity is a poorly defined concept in science.
• Traditional mathematical models struggle with physiological time series (PTS).
• Historical assumptions hinder the analysis of complex biological systems.

Purpose of the Study:

• To explore how multifractal dimensionality can redefine complexity in PTS.
• To propose a new method for analyzing physiological data.
• To uncover novel synchronization mechanisms in biological networks.

Main Methods:

• Replaced ordinary statistical events with crucial events (CEs) in PTS.
• Utilized multifractal dimensionality as a measure of complexity.
• Applied modified diffusion entropy analysis (MDEA) to empirical datasets.

Main Results:

• Demonstrated synchrony in respiration, ECG, and EEG time series after MDEA processing.
• Identified a new synchronization mechanism linked to multifractal dimension.
• Showcased that complexity measures track synchronized physiological networks over time.

Conclusions:

• Discarding historical assumptions and adopting CEs simplifies PTS analysis.
• MDEA reveals a universal synchronization mechanism across diverse physiological data.
• This framework captures principles of information exchange in physiologic organ networks.