Alcaraz Raúl, Martínez Arturo, Rieta José J
Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Cuenca, Spain.
Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Valencia, Spain.
Ann Noninvasive Electrocardiol. 2015 Sep;20(5):433-45. doi: 10.1111/anec.12240. Epub 2014 Nov 23.
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.
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.
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.
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.
与阵发性心房颤动（PAF）发作相关的心房传导缺陷的研究可通过分析体表心电图（ECG）的P波来进行。传统上，信号平均心电图大多用于此目的。然而，这种方法阻碍了对每个P波进行量化、其随时间变化的可能性，以及获取有关心律失常的补充和动态信息。本研究分析了几个时间和频率P波特征的时间进展，作为PAF发作前数小时心房传导变异性的潜在指标。
选择46例PAF患者24小时动态心电图记录中最长的窦性心律间期。接下来，提取PAF发作前2小时并分为两个1小时时段。自动描绘每个P波，并通过16个时间和频率指标进行表征，例如其持续时间、几个频带的绝对能量以及高频与低频能量比。最后，利用最小二乘线性拟合从16个特征估计每个1小时时段内的P波变异性。作为对照，还从53名年龄、性别和心率匹配的健康受试者对照组中随机选择的一组1小时ECG片段中估计相同参数。
随着PAF发作临近，所有分析指标均显示P波变异性呈增加趋势，P波持续时间和P波高频能量是最显著的个体指标。健康受试者与P波持续时间相关的线性拟合斜率α为（2.48±1.98）×10⁻²，远离PAF的ECG片段为（23.8±14.1）×10⁻²，接近PAF的ECG片段为（81.8±48.7）×10⁻²（p = 6.96×10⁻²²）。同样，P波高频能量线性拟合斜率分别为（2.42±4.97）×10⁻⁹、（54.2±107.1）×10⁻⁹和（274.2±566.1）×10⁻⁹（p = 2.85×10⁻²⁰）。单变量判别分析表明，P波持续时间和P波高频能量均可区分这三组ECG，诊断能力约为80%，通过多变量判别分析将这些指标结合起来，诊断能力提高至88%。
通过估计几个时间和频率P波特征随时间的变异性，可以在PAF发作前数小时成功量化心房传导的改变。
