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

Author(s)

Luongo, Giorgio ^[1] ; Schuler, Steffen ^[1] ; Luik, Armin ^[2] ; Almeida, Tiago P. ^{[3, 4, 5]} ; Soriano, Diogo C. ^[6] ; Dossel, Olaf ^[1] ; Loewe, Axel ^[1]

Affiliation

^[1] Karlsruhe Inst Technol, Inst Biomed Engn, D-76131 Karlsruhe - Germany

^[2] Stdt Klinikum Karlsruhe, Med Klin 4, Karlsruhe - Germany

^[3] Univ Leicester, Dept Cardiovasc Sci, Leicester, Leics - England

^[4] Univ Leicester, Sch Engn, Leicester, Leics - England

^[5] Inst Tecnol Aeronaut, Elect Engn Div, Sao Jose Dos Campos - Brazil

^[6] ABC Fed Univ, Engn Modelling & Appl Social Sci Ctr, Santo Andre, SP - Brazil

• Document type: Journal article
• Source: IEEE Transactions on Biomedical Engineering; v. 68, n. 3, p. 914-925, MAR 2021.
• Web of Science Citations: 0

Abstract

Objective: 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. Methods: 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. Results: 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. Conclusion: 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. Significance: 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. (AU)

• FAPESP's process: 19/09512-0 - Nonlinear dynamic functional connectivity analysis via recurrence quantification and its application to brain computer-interfaces
• Grantee: Diogo Coutinho Soriano
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 18/02251-4 - Characterizing persistent atrial fibrillation dynamics using computational models and recurrence quantification analysis - towards novel biomarkers for guiding therapy
• Grantee: Tiago Paggi de Almeida
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• FAPESP's process: 17/00319-8 - Atrial substrate identification in patients with chronic atrial fibrillation using multivariate statistical models and multiple attributes from atrial electrograms
• Grantee: Tiago Paggi de Almeida
• Support Opportunities: Scholarships in Brazil - Post-Doctoral