Recurrence quantification analysis applied to spatiotemporal pattern analysis in high-density mapping of human atrial fibrillation

2015 ◽  
Author(s):  
Stef Zeemering ◽  
Pietro Bonizzi ◽  
Bart Maesen ◽  
Ralf Peeters ◽  
Ulrich Schotten
Keyword(s):  
Atrial Fibrillation ◽  
Pattern Analysis ◽  
Recurrence Quantification Analysis ◽  
High Density ◽  
Spatiotemporal Pattern ◽  
Density Mapping ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Human Atrial Fibrillation

Abstract 18637: Recurrence Quantification Analysis to Distinguish Active From Passive Mechanisms of CFAEs During Catheter Ablation of Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Alex Baher ◽  
Anil K Gehi ◽  
Prabhat Kumar ◽  
Eugene Chung ◽  
Benjamin H Buck ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Recurrence Quantification Analysis ◽  
Slow Conduction ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Mean Cycle ◽  
Recurrent Patterns ◽  
Wave Break ◽  
Complex Fractionated Atrial Electrograms

Background: Ablation of complex fractionated atrial electrograms (CFAEs) is controversial, primarily because of difficulty in visually distinguishing CFAEs representing an active site of driver activity from a passive site of double potentials, wave break, and/or slow conduction. We hypothesized that CFAEs within rotors in atrial fibrillation (AF) would exhibit highly recurrent behavior compared with CFAEs remote from these driver regions. Methods: Active and passive mechanisms of CFAE formation were simulated in several 2D 7.5 x 7.5 cm modified Luo-Rudy 1 models. CFAEs within areas of rotors were considered active, while those caused by wave break, slow conduction or double potentials remote from rotors were considered passive. Clinical signals were also collected during catheter ablation of paroxysmal AF (n=8 patients). An active driver CFAE site was defined by termination of AF with ablation followed by non-inducibility. A passive site was defined as CFAE occurring remotely. Detection of CFAEs was based on mean cycle length (MCL) calculated from 4 second windows using -dV/dt for detection (40ms refractory period/10ms maximum EGM width for simulations; 45ms/15ms respectively for clinical signals). Recurrence quantification analysis (RQA) was performed on discrete time series of simulated and clinical CFAE activations. Results: RQA was performed on 20 simulated EGMs. MCL was similar in both active and passive CFAEs (74±11ms and 78±6ms respectively, p=NS), but recurrence was significantly higher in active compared to passive sites (%recurrence: 61±22% active, 4±6% passive, p<0.01). In patients with AF, the driver sites were all located within the pulmonary vein antra while passive CFAEs were remote. The MCL of CFAEs at active driver sites was similar to that of passive sites (100±13ms active, 98±17ms passive, p=NS), but recurrence was significantly higher in the active driver sites (%recurrence: 18±15% active, 2±1% passive, p=0.02). Conclusion: CFAEs may occur due to either active or passive mechanisms. Sites within rotors or focal drivers of AF are more likely to exhibit recurrent patterns. RQA may be a powerful tool to differentiate driver from bystander CFAEs enabling more efficient targeting for ablation.

UNSTABLE CARDIAC SINGULARITIES MAY LEAD TO ATRIAL FIBRILLATION

2011 ◽  
Vol 21 (04) ◽  
pp. 1141-1151 ◽  
Author(s):  
C. L. WEBBER ◽  
Z. HU ◽  
J. G. AKAR
Keyword(s):  
Atrial Fibrillation ◽  
Full Range ◽  
Central Line ◽  
Recurrence Quantification Analysis ◽  
Spiral Waves ◽  
Atrial Arrhythmias ◽  
Recurrence Plots ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Derivatives Of

A total of 53 atrial electrograms were recorded from 12 human patients diagnosed with different degrees of atrial arrhythmias and fibrillation, but not atrial flutter. The atrial waves were highly complex, noisy, nonuniform, nonlinear, and nonstationary in time and well suited for recurrence quantification analysis (RQA), spectral analysis (FFT) and atrial rate (AR) measurements. Differing degrees of atrial arrhythmias were quantified by measuring singularities in the electrograms. Singularities were defined as the maximum periods of relative isopotential squared (msec2) and presented as unfilled squares along the central line of identity (LOI) on recurrence plots. These nonsolid (unfilled) squares indicate that most singularities were unstable with noisy baselines. All measured variables were plotted against their corresponding unstable singularities. The best correlations were found for variables Vmaxand Laminar over the full range of log10(singularity). That is, the higher the degree of fibrillation the smaller the size of the singularity and the shorter Vmaxand Laminar. The shorter singularities are associated with faster spiral waves. However, since Vmaxand Laminar are direct derivatives of Singularity, this variable remains the sole best quantifier of choice to identify aberrant pacemaker regions.

scholarly journals The temporal stability of recurrence quantification analysis attributes from chronic atrial fibrillation electrograms

2018 ◽  
Vol 34 (4) ◽  
pp. 337-349
Author(s):  
Tiago Paggi de Almeida ◽  
Fernando Soares Schlindwein ◽  
João Salinet ◽  
Xin Li ◽  
Gavin Shen-Wei Chu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Temporal Stability ◽  
Chronic Atrial Fibrillation ◽  
Recurrence Quantification Analysis ◽  
Recurrence Quantification ◽  
Quantification Analysis
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