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Characterizing persistent atrial fibrillation dynamics using computational models and recurrence quantification analysis - towards novel biomarkers for guiding therapy

Grant number: 18/02251-4
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): September 01, 2018
Effective date (End): February 28, 2019
Field of knowledge:Engineering - Biomedical Engineering - Medical Engineering
Principal researcher:Takashi Yoneyama
Grantee:Tiago Paggi de Almeida
Supervisor abroad: Olaf Doessel
Home Institution: Divisão de Engenharia Eletrônica (IEE). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
Research place: Karlsruhe Institute of Technology (KIT), Germany  
Associated to the scholarship:17/00319-8 - Atrial substrate identification in patients with chronic atrial fibrillation using multivariate statistical models and multiple attributes from atrial electrograms, BP.PD


Atrial fibrillation (AF) is the most common sustained arrhythmia found in the clinical practice and is one of the main causes of stroke. The pulmonary veins (PVs) participate in AF initiation and perpetuation, and the PV isolation (PVI) performed by radiofrequency catheter ablation is effective in the therapy of the paroxysmal form of AF. PVI, however, is insufficient for persistent AF (persAF) therapy due to its complex underlying pathophysiology and spatiotemporal behaviour. Recurrence analysis has been used to explore the underlying AF dynamics during persAF with promising initial results. Recurrence plots (RPs) and recurrence quantification analysis (RQA) represent a powerful set of tools for the investigation of dynamic systems. We have recently proposed rigorous steps to properly derive the RPs and for the estimation of RQA-based attributes extracted from persAF atrial electrograms (AEGs). This investigation, however, was conducted in sequential point-by-point AEGs, which fails to provide a global atrial mapping, imposing clear limitations in the validation of the proposed method. Computational intracardiac models that simulate both atrial electrical activity and ablation procedures during AF provide an ideal tool to continue this investigation. In this work, we will further investigate the persAF underlying dynamics through the combination of computational models - provided by the Institute of Biomedical Engineering, Karlsruhe Institute of Technology - and RQA-based attributes. We expect to validate the proposed method and investigate whether these attributes can help to identify atrial regions responsible for AF perpetuation, and to and accelerate its potential integration in clinical tools to guide AF ablation in future clinical studies.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ALMEIDA, TIAGO P.; SORIANO, DIOGO C.; MASE, MICHELA; RAVELLI, FLAVIA; BEZERRA, ARTHUR S.; LI, XIN; CHU, GAVIN S.; SALINET, JOAO; STAFFORD, PETER J.; NG, G. ANDRE; et al. Unsupervised Classification of Atrial Electrograms for Electroanatomic Mapping of Human Persistent Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, v. 68, n. 4, p. 1131-1141, . (17/00319-8, 19/05192-1, 15/12799-9, 18/02251-4)
LUONGO, GIORGIO; SCHULER, STEFFEN; LUIK, ARMIN; ALMEIDA, TIAGO P.; SORIANO, DIOGO C.; DOSSEL, OLAF; LOEWE, AXEL. Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: A Computational Study. IEEE Transactions on Biomedical Engineering, v. 68, n. 3, p. 914-925, . (19/09512-0, 18/02251-4, 17/00319-8)
GODINHO, FABIO; FIM NETO, ARNALDO; BIANQUETI, BRUNO LEONARDO; DE LUCCAS, JULIA BALDI; VARJAO, EDUARDO; TERZIAN FILHO, PAULO ROBERTO; FIGUEIREDO, EBERVAL GADELHA; ALMEIDA, TIAGO PAGGI; YONEYAMA, TAKASHI; TAKAHATA, ANDRE KAZUO; et al. Spectral characteristics of subthalamic nucleus local field potentials in Parkinson's disease: Phenotype and movement matter. European Journal of Neuroscience, v. 53, n. 8, . (19/09512-0, 18/14285-0, 13/07559-3, 18/14283-8, 18/02251-4, 17/00319-8)

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