Beat-to-beat P-wave Variability Increases from Paroxysmal to Persistent Atrial Fibrillation

Computing in cardiology

Abstract

Atrial fibrillation (AF) is known to worsen over time.Beat-to-beat P-wave variability is used to evaluate the risk of developing AF, but it has not been used to monitor arrhythmia progression in a comprehensive model.The aim of this study is to create a method to measure beat-tobeat P-wave variability to evaluate AF types.ECG recordings of 5 minutes were measured on 159 AF patients.The first three principal components (PCs) of the ECG signal were added to the analysis.The temporal beat-to-beat P-wave variability was assessed through the normalized Euclidean Distance and the Similarity Index.The spatial P-wave similarity was measured as the percentage of variance explained by the first 2 PCs.A binomial logistic regression model was built for each lead and parameter, with AF type as dependent variable.To assess variability due exclusively to the P-waves, we considered, as confounding factors, other sources of ECG-variability, such as the noise level, the variability of the RR series and of the heart axis.Both temporal (e.g.0.94±0.12for paroxysmal AF and 0.85±0.28for persistent AF in lead I, p=0.001) and spatial P-wave similarities (95.35±3.29%for paroxysmal AF vs 94.44±4.14%for persistent AF, p=0.001) were significantly higher in paroxysmal than in persistent AF, suggesting them as promising tools to evaluate AF types.

Authors 7

1. Rita Laureanti

   Università della Svizzera italiana · Politecnico di Milano

   Affiliation as printed

   Center for Computational Modeling in Cardiology , Lugano , Switzerland

   Politecnico di Milano , Milan , Italy

   Center for Computational Modeling in Cardiology, Lugano, Switzerland

   Politecnico di Milano, Milan, Italy

2. Stef Zeemering

   Maastricht University

   Affiliation as printed

   Cardiovascular Research Institute Maastricht (CARIM) , Maastricht , the Netherlands

   Cardiovascular Research Institute Maastricht (CARIM), Maastricht, the Netherlands

3. Matthias Daniel Zink Aachen

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   RWTH University Hospital Aachen , Aachen , Germany

   RWTH University Hospital Aachen, Aachen, Germany

4. Valentina Corino

   Politecnico di Milano

   Affiliation as printed

   Politecnico di Milano , Milan , Italy

   Politecnico di Milano, Milan, Italy

5. Angelo Auricchio

   Istituto Cardiocentro Ticino · Università della Svizzera italiana

   Affiliation as printed

   Cardiocentro Ticino , Lugano , Switzerland

   Center for Computational Modeling in Cardiology , Lugano , Switzerland

   Cardiocentro Ticino, Lugano, Switzerland

   Center for Computational Modeling in Cardiology, Lugano, Switzerland

6. Luca Tommaso Mainardi

   Politecnico di Milano

   Affiliation as printed

   Politecnico di Milano , Milan , Italy

   Politecnico di Milano, Milan, Italy

7. Ulrich Schotten

   Maastricht University

   Affiliation as printed

   Cardiovascular Research Institute Maastricht (CARIM) , Maastricht , the Netherlands

   Cardiovascular Research Institute Maastricht (CARIM), Maastricht, the Netherlands