ArticleFrontiers in network physiology2025
Complexity synchronization analysis of neurophysiological data: Theory and methods.
Article in Frontiers in network physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- From visibility graphs to cognition.Frontiers in network physiology · 2026Article
- Cross-wavelet analysis allows obtaining high temporal and frequency resolution in heart rate synchrony analysis.Frontiers in network physiology · 2026Article
- State Space Correspondence and Cross-Entropy Methods in the Assessment of Bidirectional Cardiorespiratory Coupling in Heart Failure.Entropy (Basel, Switzerland) · 2025Article
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9 authors.
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Abstract
Introduction: We present a theoretical foundation based on the spontaneous self-organized temporal criticality (SOTC) and multifractal dimensionality Methods: We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS), and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network-of-organ-networks (NoONs). An automated parameter selection process is proposed that relies on the Kolmogorov-Smirnov statistic to properly choose stripe sizes which are crucial in the MDEA analysis using synthetic duration times derived from the Mittag-Leffler map, shows the strength of KS-based stripe size selection to track changes in the IPL parameter Results: Results from processing these datasets show that the complexity of brain, heart, and lung ONTS co-vary over time during cognitive task performance in 44% of subjects, while complexity of brain-heart interactions significantly co-vary in 85% of subjects. Discussion: We conclude that certain principles, guidelines, and strategies for the application of MDEA analysis need further consideration. We conclude with a summary of the MDEA's limitations and future research directions.
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