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

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±0.12 for paroxysmal AF and 0.85±0.28 for 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.

Extended ECG Improves Classification of Paroxysmal and Persistent Atrial Fibrillation Based on P- and f-Waves

“…• Spatial similarity: Total percentage of variance explained by the 2 first principal components computing applying PCA on each beat. A median value of all the beats was computed (Di Marco et al, 2012;Meo et al, 2018;Laureanti et al, 2020b).…”

“…Other sources of ECG-related variability (heart rate, heart axis, and noise level) were also measured as confounding factors, as described earlier (Laureanti et al, 2020b):…”