A Multi-Variate Predictability Framework to Assess Invasive Cardiac Activity and Interactions during Atrial Fibrillation

6533b82ffe1ef96bd1294727

RESEARCH PRODUCT

A Multi-Variate Predictability Framework to Assess Invasive Cardiac Activity and Interactions during Atrial Fibrillation

Pablo Laguna Alejandro Alcaine Giandomenico Nollo Alessandro Cristoforetti Juan Pablo Martinez Luca Faes Flavia Ravelli Michela Masè

subject

medicine.medical_specialty Computer science 0206 medical engineering Atrial fibrillation (AF)Biomedical Engineering Cardiac activity 02 engineering and technology 030204 cardiovascular system & hematology Intracardiac injection multi-variate autoregressive (MVAR) modeling 03 medical and health sciences 0302 clinical medicine Heart Conduction System Internal medicine Atrial Fibrillation multielectrode catheter medicine Humans Computer Simulation Predictability Models Statistical business.industry Body Surface Potential Mapping Models Cardiovascular Pattern recognition Atrial fibrillation medicine.disease 020601 biomedical engineering Random variate Autoregressive model Data Interpretation Statistical bipolar electrograms (EGMs)Multivariate Analysis Settore ING-INF/06 - Bioingegneria Elettronica E Informatica Cardiology Granger causality (GC)Artificial intelligence business

description

Objective: This study introduces a predictability framework based on the concept of Granger causality (GC), in order to analyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). Methods: GC-based interactions were studied using a three-electrode analysis scheme with multi-variate autoregressive models of the involved preprocessed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial signals acquired from patients. Results: The results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. Conclusion: The proposed framework provides information on the underlying activation and regularity, does not require activation detection or postprocessing algorithms and is applicable for the analysis of any multielectrode catheter. Significance: The proposed framework can potentially help to guide catheter ablation interventions of AF.

year	journal	country	edition	language
2017-01-01

10.1109/tbme.2016.2592953 http://hdl.handle.net/10447/271879