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Non-linear time series analysis: methods and applications to atrial fibrillation
B P Hoekstra1, C G Diks, M A Allessie
1Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Annali Dell'Istituto Superiore Di Sanita
|March 14, 2002
Summary
Non-linear time series analysis characterizes atrial fibrillation electrograms by examining dynamical behavior. These methods offer a novel framework for understanding complex cardiac electrical activity during fibrillation.
Area of Science:
- Cardiology
- Non-linear Dynamics
- Statistical Time Series Analysis
Background:
- Atrial fibrillation (AF) presents complex electrogram dynamics.
- Characterizing these dynamics is crucial for understanding AF.
- Existing methods may not fully capture the complexity of AF electrograms.
Purpose of the Study:
- To apply non-linear time series analysis methods to characterize atrial fibrillation (AF) electrograms.
- To utilize concepts from non-linear dynamical systems theory for AF analysis.
- To demonstrate the utility of reconstruction density in phase space for AF electrogram analysis.
Main Methods:
- Application of non-linear statistical time series analysis.
- Utilizing empirical reconstruction density in reconstructed phase space.
- Employing three specific non-linear time series tests: time reversibility, dynamical behavior detection during pharmacological conversion, and Granger causality for information transport.
Main Results:
- Demonstrated time reversibility in atrial electrograms during paroxysmal AF in patients.
- Detected differences in dynamical behavior during pharmacological conversion of sustained AF in goats.
- Identified couplings and information transport in the atria during AF using Granger causality.
Conclusions:
- Non-linear time series analysis provides a robust framework for characterizing AF electrograms.
- Reconstruction density analysis offers valuable insights into the dynamics of cardiac electrical activity during fibrillation.
- These methods are applicable beyond deterministic chaos to general time series contexts.