Raúl Alcaraz1, Arturo Martínez1, José J Rieta2. 1. Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Cuenca, Spain. 2. Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Valencia, Spain.
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. CONCLUSION: 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.
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. CONCLUSION: 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: Valentin Fuster; Lars E Rydén; Davis S Cannom; Harry J Crijns; Anne B Curtis; Kenneth A Ellenbogen; Jonathan L Halperin; G Neal Kay; Jean-Yves Le Huezey; James E Lowe; S Bertil Olsson; Eric N Prystowsky; Juan Luis Tamargo; L Samuel Wann; Sidney C Smith; Silvia G Priori; N A Mark Estes; Michael D Ezekowitz; Warren M Jackman; Craig T January; James E Lowe; Richard L Page; David J Slotwiner; William G Stevenson; Cynthia M Tracy; Alice K Jacobs; Jeffrey L Anderson; Nancy Albert; Christopher E Buller; Mark A Creager; Steven M Ettinger; Robert A Guyton; Jonathan L Halperin; Judith S Hochman; Frederick G Kushner; Erik Magnus Ohman; William G Stevenson; Lynn G Tarkington; Clyde W Yancy Journal: Circulation Date: 2011-03-07 Impact factor: 29.690
Authors: T Hiraki; H Ikeda; M Ohga; T Hamada; I Kubara; T Yoshida; H Ajisaka; A Tanabe; M Kanahara; T Imaizumi Journal: Pacing Clin Electrophysiol Date: 1998-01 Impact factor: 1.976
Authors: S Vikman; T H Mäkikallio; S Yli-Mäyry; S Pikkujämsä; A M Koivisto; P Reinikainen; K E Airaksinen; H V Huikuri Journal: Circulation Date: 1999-11-16 Impact factor: 29.690
Authors: P Papageorgiou; K Monahan; N G Boyle; M J Seifert; P Beswick; J Zebede; L M Epstein; M E Josephson Journal: Circulation Date: 1996-08-01 Impact factor: 29.690
Authors: Fredrik Holmqvist; Pyotr G Platonov; Jonas Carlson; Wojciech Zareba; Arthur J Moss Journal: Ann Noninvasive Electrocardiol Date: 2009-07 Impact factor: 1.468
Authors: Aikaterini Vraka; José Moreno-Arribas; Juan M Gracia-Baena; Fernando Hornero; Raúl Alcaraz; José J Rieta Journal: J Cardiovasc Dev Dis Date: 2022-06-01
Authors: Aikaterini Vraka; Vicente Bertomeu-González; Lorenzo Fácila; José Moreno-Arribas; Raúl Alcaraz; José J Rieta Journal: J Pers Med Date: 2022-03-14