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

作者：Alcaine Alejandro^*; Mase Michela; Cristoforetti Alessandro; Ravelli Flavia; Nollo Giandomenico; Laguna Pablo; Pablo Martinez Juan; Faes Luca

来源：IEEE Transactions on Biomedical Engineering, 2017, 64(5): 1157-1168.

DOI：10.1109/TBME.2016.2592953

• Atrial fibrillation (AF)
• bipolar electrograms (EGMs)
• Granger causality (GC)
• multielectrode catheters
• multi-variate autoregressive (MVAR) modeling

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 andmap 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.

• 出版日期2017-5