title: The P Wave Time-Frequency Variability Reflects Atrial Conduction Defects before Paroxysmal Atrial Fibrillation.
abstract: BACKGROUND
  The study of atrial conduction defects associated with the onset of paroxysmal atrial fibrillation (PAF) can be addressed by analyzing the P wave from the surface electrocardiogram (ECG). Traditionally, signal-averaged ECGs have been mostly used for this purpose. However, this alternative hinders the possibility to quantify every single P wave, its variability over time, as well as to obtain complimentary and evolving information about the arrhythmia. This work analyzes the time progression of several time and frequency P wave features as potential indicators of atrial conduction variability several hours preceding the onset of PAF.
  
  
  METHODS
  The longest sinus rhythm interval from 24-hour Holter recordings of 46 PAF patients was selected. Next, the 2 hours before the onset of PAF were extracted and divided into two 1-hour periods. Every single P wave was automatically delineated and characterized by 16 time and frequency metrics, such as its duration, absolute energy in several frequency bands and high-to-low-frequency energy ratios. Finally, the P wave variability over each 1-hour period was estimated from the 16 features making use of a least-squares linear fitting. As a reference, the same parameters were also estimated from a set of 1-hour ECG segments randomly chosen from a control group of 53 healthy subjects age-, gender-, and heart rate-matched.
  
  
  RESULTS
  All the analyzed metrics provided an increasing P wave variability trend as the onset of PAF approximated, being P wave duration and P wave high-frequency energy the most significant individual metrics. The linear fitting slope α associated with P wave duration was (2.48 ± 1.98)×10(-2) for healthy subjects, (23.8 ± 14.1)×10(-2) for ECG segments far from PAF and for (81.8 ± 48.7)×10(-2) ECG segments close to PAF p = 6.96×10(-22) . Similarly, the P wave high-frequency energy linear fitting slope was (2.42 ± 4.97)×10(-9) , (54.2 ± 107.1)×10(-9) and (274.2 ± 566.1)×10(-9) , respectively (p = 2.85×10(-20) ). A univariate discriminant analysis provided that both P wave duration and P wave high-frequency energy could discern among the three ECG sets with diagnostic ability around 80%, which was improved up to 88% by combining these metrics in a multivariate discriminant analysis.
  
  
  CONCLUSIONS
  Alterations in atrial conduction can be successfully quantified several hours before the onset of PAF by estimating variability over time of several time and frequency P wave features.
authors.affiliation: Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Cuenca, Spain.
authors.forename: Raúl
authors.initials: R
authors.lastname: Alcaraz
authors.affiliation: Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Cuenca, Spain.
authors.forename: Arturo
authors.initials: A
authors.lastname: Martínez
authors.affiliation: Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Valencia, Spain.
authors.forename: José J
authors.initials: JJ
authors.lastname: Rieta
mesh_terms: D000368:Aged; D001145:Arrhythmias, Cardiac / Q000150:complications / Q000175:diagnosis*; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000209:etiology; D018880:Atrial Premature Complexes / Q000150:complications / Q000175:diagnosis; D053840:Brugada Syndrome; D000075224:Cardiac Conduction System Disease; D015716:Electrocardiography, Ambulatory; D005260:Female; D006329:Heart Conduction System / Q000002:abnormalities*; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged
keywords: atrial conduction; paroxysmal atrial fibrillation; surface P wave; time and frequency analysis