Noninvasive characterization of atrial fibrillation: an animal model study

• Grant number: 22/04567-4
• Support Opportunities: Scholarships in Brazil - Master
• Start date: June 01, 2022
• End date: December 31, 2022
• Field of knowledge: Engineering - Biomedical Engineering - Bioengineering
• Principal Investigator: João Loures Salinet Júnior
• Grantee: Daniel Cavalcante de Oliveira
• Host Institution: Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Associated research grant: 18/25606-2 - Accurate mapping of cardiac fibrillation activity: an experimental contribution, AP.JP

Abstract

Atrial fibrillation (AF) is the most frequent sustained cardiac arrhythmia in clinical practice, affecting between 1 and 2% of the world population. This disorder has high morbidity and mortality, and has become a chronic non-infectious cardiovascular epidemic, making it an important public health problem, and consumption of health resources. Recently, basic and clinical research has made great advances in the improvement of the diagnosis and treatment of AF, and its mechanism has been gradually elucidated, but not fully clarified. During AF, the interpretation of signals and maps, obtained from commercial electrical mapping systems, is complex and uncertain, hindering the correct characterization and location of arrhythmogenic sources, reducing the efficacy of ablation treatment. The correct identification of the type of mechanism and its location is the current challenge of electrophysiologists. Due to the complexity of this arrhythmia and great sensitivity to errors by current commercial systems, it is important that validations of proposed methods are first validated under controlled experiments by mimetizing clinical situations. The main objective of this project is to develop an experimental model in situ of AF induced with electrical stimulation for customization of electrocardiographic metrics and maps obtained by body surface potential mapping (BSPM). This characterization may allow the identification of patterns of AF mechanisms noninvasively, which might help in future, electrophysiologists to further understand this arrhythmia and also in the patient's management. For this, it will be necessary to develop a Graphical Interface in Matlab (or Python), for monitoring and management of the experimental setup. The setup consists of an electrical mapping system, panoramic optical mapping system, Langendorff-type isolated heart reperfusion system, rotation system and electrical stimulation. After validation of the graphical interface and experimental setup, the experiments will be carried out with isolated rabbit hearts. The induction of AF will be by a protocol of restitution by standard pulse train (S1-S1) in the left atrium. The acquisition of biopotentials in the epicardium will be performed by contact unipolar electrodes. The acquisition of non-invasive electrical activity will be performed through electrodes distributed equally between the faces of a hexagonal acrylic and translucent tank, with heated Tyrode solution (37oC) inside. Thus, the heart will be submerged in the tank with the nutrient solution inside, in order to maintain its physiological activity and drive its electrical impulses to the electrodes of the tank face. Signal analysis and 3D maps will be performed using Matlab Version 9.7 (R2019) (Mathworks, Inc.) or Python. Metrics and maps in the time and frequency domain will be calculated from the electrical signals of the epicardium and non-invasive. This will allow the non-invasive classification of AF patterns and the characterization of sand-time patterns. Through a pipeline of pre-processing and post-processing techniques to be applied to the signals obtained by non-invasive electrical mapping, it is intended to generate non-invasive maps during AF more realistic with the pathophysiology of AF. This research project, is an interdisciplinary study which face areas of engineering and health, such understanding important aspects in the area of electrophysiological biological signal processing, hardware and animal experimentation, ranging from the validation of techniques to a possible implementation in medical equipment.