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Causality in physiological signals.

Andreas Müller1, Jan F Kraemer, Thomas Penzel

  • 1Department of Physics, Cardiovascular Physics, Humboldt-Universität zu Berlin, Berlin, Germany.

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This review explores various coupling measures for analyzing cardiovascular system interactions. It classifies methods from correlation to symbolic dynamics to aid researchers in selecting appropriate tools for physiological analyses.

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Area of Science:

  • Cardiovascular physiology and time series analysis.
  • Biomedical engineering and signal processing.
  • Non-invasive physiological monitoring.

Background:

  • Cardiovascular diseases are the leading cause of death globally, necessitating a deep understanding of the cardiovascular system.
  • Non-invasive measurement techniques are crucial for obtaining physiological data with minimal patient discomfort.
  • Coupling measures are increasingly important for analyzing interactions within complex biological systems.

Purpose of the Study:

  • To provide a comprehensive overview of coupling measures for cardiovascular time series analysis.
  • To classify coupling measures based on their origin and capabilities for physiological applications.
  • To guide researchers in selecting the most appropriate analytical tools for their specific needs.

Main Methods:

  • Review of classical correlation measures.
  • Exploration of Granger-causality-based tools, entropy-based techniques (e.g., momentary information transfer), and nonlinear prediction measures (e.g., mutual prediction).
  • Inclusion of symbolic dynamics (e.g., symbolic coupling traces), synchronization/coordination analysis (e.g., synchrogram, coordigram), and time-dependent coupling detection.

Main Results:

  • Coupling measures offer insights into physiological interactions such as cardiorespiratory and neuro-cardiac coupling.
  • Different methods provide varying information regarding coupling direction, strength, and time lags.
  • A toy model is used to illustrate the essential features and performance of representative coupling measures.

Conclusions:

  • This review categorizes diverse coupling measures, aiding researchers in understanding their applications in cardiovascular research.
  • The study offers guidance on the selection of appropriate methods for analyzing physiological interactions.
  • Summarizes the performance of each measure, providing practical advice for their utilization.