Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue

Area of Science:

• Computational Biology
• Cardiac Electrophysiology
• Nonlinear Dynamics

Background:

• Cardiac arrhythmias, such as fibrillation, pose significant health risks and are complex to model.
• Understanding the dynamics of cardiac tissue is crucial for developing effective treatments.

Purpose of the Study:

• To apply Recurrence Quantification Analysis (RQA) and related metrics to characterize and differentiate various cardiac arrhythmic patterns in computer simulations.
• To identify specific RQA metrics capable of pinpointing key elements like rotor tips and classifying distinct arrhythmia types.

Main Methods:

• Computer simulations of cardiac tissue dynamics.
• Application of Recurrence Quantification Analysis (RQA) combined with entropy measures and organization indices.
• Analysis of membrane potential time series and spatial correlations.

Main Results:

• Four classic arrhythmic patterns were simulated and characterized: sustained re-entry, meandering spiral waves, fibrillation, and rotor-wavebreak dynamics.
• RQA metrics successfully differentiated regions of regular and chaotic electrical propagation within simulations.
• Specific RQA metrics identified rotor tip locations and enabled the separation of different arrhythmia types in a feature space.

Conclusions:

• RQA-based metrics provide a powerful tool for analyzing complex cardiac dynamics and classifying arrhythmias.
• The identified metrics show potential for automatic classification systems, distinguishing between different mechanisms of fibrillation.
• This approach offers practical applicability for understanding and potentially diagnosing cardiac arrhythmias.