| Abstract: |
| Physiological signals such as heart rate, blood pressure, and respiration reflect the dynamics of a complex and tightly coupled regulatory system. Their analysis therefore calls for mathematical methods capable of capturing nonlinear interactions, multiscale structure, and temporal variability. Among such approaches, ordinal pattern analysis and topological methods, including persistent homology, provide promising tools for investigating the organization of physiological data.The aim of this work is to study how these methods can be applied to the analysis of interactions within the cardiorespiratory system, with particular emphasis on the role of respiration in shaping cardiovascular dynamics. By comparing selected signal characteristics across physiological states and between healthy and pathological conditions, we seek to identify features that may indicate changes in regulatory mechanisms |
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