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Using noise to determine cardiac restitution with memory.

Shu Dai1, James P Keener

  • 1Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio 43210, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 26, 2012
PubMed
Summary

This study introduces a novel method using noisy pacing cycles to model cardiac cell dynamics. This approach helps detect critical changes in heart rhythm, known as alternans, which are vital for understanding cardiac health.

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Area of Science:

  • Cardiovascular physiology
  • Computational biology
  • Nonlinear dynamics

Background:

  • Cardiac pacing cycle variations, like heart rate variability, are well-documented.
  • Mathematical models exist for paced cardiac cell electrical activity.
  • The impact of pacing cycle stochasticity on these models is unexplored.

Purpose of the Study:

  • To develop a method for approximating cardiac cell dynamics using stochastic pacing cycles.
  • To utilize these approximate models for detecting bifurcations leading to alternans.
  • To bridge the gap in understanding how pacing variability influences cardiac electrical models.

Main Methods:

  • Employing stochastic differential equations to model noisy pacing cycles.
  • Developing approximate models of cardiac cell dynamics based on pacing variability.
  • Implementing bifurcation analysis techniques to identify transitions to alternans.
  • Simulating cardiac cell electrical activity under varied pacing conditions.

Main Results:

  • Successfully generated approximate models capturing cardiac cell dynamics under stochastic pacing.
  • Demonstrated the ability of these models to detect bifurcations to alternans.
  • Quantified the influence of pacing noise on the onset of alternans.
  • Provided a framework for analyzing cardiac dynamics with realistic pacing variability.

Conclusions:

  • Stochasticity in pacing cycles can be effectively used to model cardiac cell dynamics.
  • The developed method reliably detects bifurcations to alternans, offering insights into arrhythmogenesis.
  • This work provides a new tool for studying cardiac electrophysiology and potential therapeutic targets.
  • Future research can extend this method to more complex cardiac models and pathologies.