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Updated: Feb 8, 2026
11:21
Robotic Ablation of Atrial Fibrillation
Published on: May 29, 2015
20.2K
Is human atrial fibrillation stochastic or deterministic?-Insights from missing ordinal patterns and causal
Konstantinos N Aronis1, Ronald D Berger1, Hugh Calkins1
1Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA.
Chaos (Woodbury, N.Y.)
|July 2, 2018
Summary
Human atrial fibrillation (AF) dynamics are not purely stochastic, suggesting a deterministic or nonlinear stochastic process. This finding supports developing mathematical tools for predicting and controlling AF.
Area of Science:
- Cardiology
- Nonlinear Dynamics
- Biophysics
Background:
- The underlying mechanism maintaining human atrial fibrillation (AF) remains unclear.
- Distinguishing deterministic from stochastic cardiac dynamics is challenging with traditional methods, especially with noisy clinical data.
Purpose of the Study:
- To investigate whether human atrial fibrillation (AF) dynamics result from a stochastic or deterministic process.
- To apply the missing ordinal patterns (MOPs) method using Bandt-Pompe (BP) symbolization to analyze AF dynamics.
Main Methods:
- Analysis of 38 intracardiac atrial electrograms from 10 AF patients.
- Conversion of atrial-to-atrial (AA) interval time series to BP symbolic representation.
- Comparison of MOPs and MOP decay rates between original AF data and iterative amplitude-adjusted Fourier-transform (IAAFT) surrogate data.
Main Results:
- Human AF exhibited significantly more MOPs (2.7 ± 1.18) than surrogate data (0.39 ± 0.28).
- The median MOP decay rate in AF (6.58 × 10⁻³) was significantly lower than in surrogates (7.79 × 10⁻³).
- Causal entropy-complexity plane analysis positioned human AF within the deterministic region, distinct from fractional Brownian motion.
Conclusions:
- Human AF dynamics are not explained by linear stochastic processes or fractional noise.
- Findings suggest AF dynamics are either deterministic or nonlinear stochastic.
- Results advocate for advanced mathematical modeling for predictive AF control.
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