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

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

Risultato della ricerca: Article

6 Citazioni (Scopus)

Abstract

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.
Lingua originaleEnglish
pagine (da-a)1157-1168
Numero di pagine12
RivistaIEEE Transactions on Biomedical Engineering
Volume64
Stato di pubblicazionePublished - 2017

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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