Literature DB >> 25758369

Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation.

Raúl Alcaraz1, Arturo Martínez2, José J Rieta3.   

Abstract

A normal cardiac activation starts in the sinoatrial node and then spreads throughout the atrial myocardium, thus defining the P-wave of the electrocardiogram. However, when the onset of paroxysmal atrial fibrillation (PAF) approximates, a highly disturbed electrical activity occurs within the atria, thus provoking fragmented and eventually longer P-waves. Although this altered atrial conduction has been successfully quantified just before PAF onset from the signal-averaged P-wave spectral analysis, its evolution during the hours preceding the arrhythmia has not been assessed yet. This work focuses on quantifying the P-wave spectral content variability over the 2h preceding PAF onset with the aim of anticipating as much as possible the arrhythmic episode envision. For that purpose, the time course of several metrics estimating absolute energy and ratios of high- to low-frequency power in different bands between 20 and 200Hz has been computed from the P-wave autoregressive spectral estimation. All the analyzed metrics showed an increasing variability trend as PAF onset approximated, providing the P-wave high-frequency energy (between 80 and 150Hz) a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF and patients less than 1h close to a PAF episode. This discriminant power was similar to that provided by the most classical time-domain approach, i.e., the P-wave duration. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 88.07%, thus constituting a reliable noninvasive harbinger of PAF onset with a reasonable anticipation. The information provided by this methodology could be very useful in clinical practice either to optimize the antiarrhythmic treatment in patients at high-risk of PAF onset and to limit drug administration in low risk patients.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Autoregressive modeling; Electrocardiogram; Spectral analysis; Surface P-wave

Mesh:

Year:  2015        PMID: 25758369     DOI: 10.1016/j.cmpb.2015.01.006

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  3 in total

1.  Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

Authors:  Ioannis A Kakadiaris; Michalis Vrigkas; Albert A Yen; Tatiana Kuznetsova; Matthew Budoff; Morteza Naghavi
Journal:  J Am Heart Assoc       Date:  2018-11-20       Impact factor: 5.501

2.  Splitting the P-Wave: Improved Evaluation of Left Atrial Substrate Modification after Pulmonary Vein Isolation of Paroxysmal Atrial Fibrillation.

Authors:  Aikaterini Vraka; Vicente Bertomeu-González; Fernando Hornero; Aurelio Quesada; Raúl Alcaraz; José J Rieta
Journal:  Sensors (Basel)       Date:  2021-12-31       Impact factor: 3.576

3.  The Dissimilar Impact in Atrial Substrate Modificationof Left and Right Pulmonary Veins Isolation after Catheter Ablation of Paroxysmal Atrial Fibrillation.

Authors:  Aikaterini Vraka; Vicente Bertomeu-González; Lorenzo Fácila; José Moreno-Arribas; Raúl Alcaraz; José J Rieta
Journal:  J Pers Med       Date:  2022-03-14
  3 in total

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