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

Abstract

The temporal behavior of atrial electrograms (AEGs) collected during persistent atrial fibrillation (persAF) directly affects ablative treatment outcomes. We investigated different durations of AEGs collected during persAF using recurrence quantification analysis (RQA). Methods: 797 bipolar AEGs with different durations (from 0.5 s to 8 s) from 18 patients were investigated. Four RQA-based attributes were evaluated based on AEG durations: determinism (DET); recurrence rate (RR); laminarity (LAM); and diagonal lines' entropy (ENTR). The Spearman correlation (ρ) between each duration versus 8 s was calculated. AEG classification was performed following the CARTO criteria (Biosense Webster) and receiving operating characteristic (ROC) curves were created for the RQA variables. Results: The RQA variables successfully discriminated the AEGs: the area under the ROC curves were as high as 0.70 for AEGs with 3.5 s or greater. Three types of AEGs were found using these variables: normal, fractionated and temporally unstable. The number of unstable AEGs decreased with longer AEG segments. Different AEG durations significantly affected the RQA variables (P<0.0001), with no statistical difference between the durations 6 s, 7 s and 8 s for DET, LAM and ENTR, and no difference between 7 s and 8 s for RR (P<0.0001). AEGs with 3 s or longer have shown ρ ≥ 80% for all variables. Conclusion: The RQA variables have been shown effective in the characterization of AEGs collected during persAF with a shorter duration than current recommendations, which motivates their use for the characterization of atrial substrate during persAF ablation.

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

“…Nine of the 11 most selected features were obtained with RQA or ratios of PCA eigenvalues ( Figure 4 A), probably owing to their sensitivity in detecting changes in the dynamic behavior over time. 47 We observed that AF drivers located in the PVs produced a more regular activity than the extra-PV cases ( Figure 1 A– 1 B.3). In fact, in the simulated episodes, the irregular activity driven by PV cases was limited to a small portion of tissue owing to the presence of anatomical obstacles (eg, the PV sleeves) that prevented the driving mechanisms from meandering to the remaining parts of the atria.…”

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

“…In the past, RQA has been used to analyze the relationship between atrial rate and spectral centre frequency 13 , to quantitatively analyse CFAEs 14 , 15 , to classify atrial electrograms as normal, fractionated or temporally unstable 16 – 18 , and to examine dynamics before and after ablative treatment 19 . This is possible even for short electrogram sequences 17 , 20 .…”

Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational Study

“…Since then, RQA has been extensively used for characterizing the dynamics of heart rate variability [20], [21], cardiac restitution [22], or even combined with machine learning techniques for sudden cardiac death stratification [23] and ECG-based arrhythmia classification [24], [25], among other applications. RQA has been used to specifically characterize the dynamics of intracardiac signals during cardiac disorders [26], [27], [28], [29]. These investigations have shown that RQA-based features represent a promising set of tools to identify phase transitions and discriminate different EP characteristics related to the atrial tissue.…”