{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:47:06Z","timestamp":1787500026318,"version":"build-2736575974"},"reference-count":40,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2014,11,23]],"date-time":"2014-11-23T00:00:00Z","timestamp":1416700800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"Consejeria Educacion, Cultura y Deportes, Junta de Comunidades de Castilla-La Mancha","award":["PPII11\u00e2\u20ac\u201c0194\u00e2\u20ac\u201c8121"],"award-info":[{"award-number":["PPII11\u00e2\u20ac\u201c0194\u00e2\u20ac\u201c8121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Noninvasive Electrocardiol"],"published-print":{"date-parts":[[2015,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u2010averaged 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The longest sinus rhythm interval from 24\u2010hour Holter recordings of 46 PAF patients was selected. Next, the 2 hours before the onset of PAF were extracted and divided into two 1\u2010hour 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\u2010to\u2010low\u2010frequency energy ratios. Finally, the P wave variability over each 1\u2010hour period was estimated from the 16 features making use of a least\u2010squares linear fitting. As a reference, the same parameters were also estimated from a set of 1\u2010hour ECG segments randomly chosen from a control group of 53 healthy subjects age\u2010, gender\u2010, and heart rate\u2010matched.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u2010frequency energy the most significant individual metrics. The linear fitting slope \u03b1 associated with P wave duration was (2.48 \u00b1 1.98)\u00d710\n                      <jats:sup>\u20102<\/jats:sup>\n                      for healthy subjects, (23.8 \u00b1 14.1)\u00d710\n                      <jats:sup>\u20102<\/jats:sup>\n                      for ECG segments far from PAF and for (81.8 \u00b1 48.7)\u00d710\n                      <jats:sup>\u20102<\/jats:sup>\n                      ECG segments close to PAF\n                      <jats:italic>p<\/jats:italic>\n                      = 6.96\u00d710\n                      <jats:sup>\u201022<\/jats:sup>\n                      . Similarly, the P wave high\u2010frequency energy linear fitting slope was (2.42 \u00b1 4.97)\u00d710\n                      <jats:sup>\u20109<\/jats:sup>\n                      , (54.2 \u00b1 107.1)\u00d710\n                      <jats:sup>\u20109<\/jats:sup>\n                      and (274.2 \u00b1 566.1)\u00d710\n                      <jats:sup>\u20109<\/jats:sup>\n                      , respectively (\n                      <jats:italic>p<\/jats:italic>\n                      = 2.85\u00d710\n                      <jats:sup>\u201020<\/jats:sup>\n                      ). A univariate discriminant analysis provided that both P wave duration and P wave high\u2010frequency 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/anec.12240","type":"journal-article","created":{"date-parts":[[2014,11,23]],"date-time":"2014-11-23T22:49:17Z","timestamp":1416782957000},"page":"433-445","source":"Crossref","is-referenced-by-count":13,"title":["The P Wave Time\u2010Frequency Variability Reflects Atrial Conduction Defects before Paroxysmal Atrial Fibrillation"],"prefix":"10.1111","volume":"20","author":[{"given":"Ra\u00fal","family":"Alcaraz","sequence":"first","affiliation":[{"name":"Innovation in Bioengineering Research Group University of Castilla\u2010La Mancha, Cuenca  Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arturo","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Innovation in Bioengineering Research Group University of Castilla\u2010La Mancha, Cuenca  Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 J.","family":"Rieta","sequence":"additional","affiliation":[{"name":"Biomedical Synergy, Electronic Engineering Department Universidad Polit\u00e9cnica de Valencia, Valencia  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