Abstract
Atrial flutter (AFl) is a common arrhythmia that can be categorized according to different self-sustained electrophysiological mechanisms. The non-invasive discrimination of such mechanisms would greatly benefit ablative methods for AFl therapy as the driving mechanisms would be described prior to the invasive procedure, helping to guide ablation. In the present work, we sought to implement recurrence quantification analysis (RQA) on 12-lead ECG signals from a computational framework to discriminate different electrophysiological mechanisms sustaining AFl. 20 different AFl mechanisms were generated in 8 atrial models and were propagated into 8 torso models via forward solution, resulting in 1,256 sets of 12-lead ECG signals. Principal component analysis was applied on the 12-lead ECGs, and six RQA-based features were extracted from the most significant principal component scores in two different approaches: individual component RQA and spatial reduced RQA. In both approaches, RQA-based features were significantly sensitive to the dynamic structures underlying different AFl mechanisms. Hit rate as high as 67.7% was achieved when discriminating the 20 AFl mechanisms. RQA-based features estimated for a clinical sample suggested high agreement with the results found in the computational framework. RQA has been shown an effective method to distinguish different AFl electrophysiological mechanisms in a non-invasive computational framework. A clinical 12-lead ECG used as proof of concept showed the value of both the simulations and the methods. The non-invasive discrimination of AFl mechanisms helps to delineate the ablation strategy, reducing time and resources required to conduct invasive cardiac mapping and ablation procedures.
Highlights
A TRIAL flutter (AFl) is a common reentrant arrhythmia with 200,000 new annual cases estimated for the U.S population [1]
The results suggest that recurrence quantification analysis (RQA) is effective in characterizing and discriminating the dynamic structures from distinct AFl mechanisms, and might help to define novel protocols for clinical planning and ablation strategy
Up to five principal component (PC) were tested in descending order of total variance explanation, in which a PCS was added as a new dimension at each iteration, starting with the two main dimensions - PCS 1 and 2
Summary
A TRIAL flutter (AFl) is a common reentrant arrhythmia with 200,000 new annual cases estimated for the U.S population [1]. This arrhythmia is characterized by self-sustained mechanisms and electrical signals that regularly propagate along various physiological pathways [2]–[4]. The mechanisms perpetuating AFl have been extensively investigated and are relatively well known. This includes right atrial (RA) tachycardia [5]–[7] and left atrial (LA) AFl forms [8]–[10], occurrences after ablation for atrial fibrillation (AFib) [11]–[13] and macro reentrant mechanisms [14].
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