Filos Dimitrios, Chouvarda Ioanna, Tachmatzidis Dimitris, Vassilikos Vassilios, Maglaveras Nicos
Laboratory of Computing and Medical Informatics, Aristotle University of Thessaloniki, Box 323, 54124, Thessaloniki, Greece.
3rd Cardiology Department, Aristotle University of Thessaloniki, Greece.
Comput Methods Programs Biomed. 2017 Nov;151:111-121. doi: 10.1016/j.cmpb.2017.08.016. Epub 2017 Aug 24.
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. The initiation and the perpetuation of AF is linked with phenomena of atrial remodeling, referring to the modification of the electrical and structural characteristics of the atrium. P-wave morphology analysis can reveal information regarding the propagation of the electrical activity on the atrial substrate. The purpose of this study is to investigate patterns on the P-wave morphology that may occur in patients with Paroxysmal AF (PAF) and which can be the basis for distinguishing between PAF and healthy subjects.
Vectorcardiographic signals in the three orthogonal axes (X, Y and Z), of 3-5 min duration, were analyzed during SR. In total 29 PAF patients and 34 healthy volunteers were included in the analysis. These data were divided into two distinct datasets, one for the training and one for the testing of the proposed approach. The method is based on the identification of the dominant and the secondary P-wave morphology by combining adaptive k-means clustering of morphologies and a beat-to-beat cross correlation technique. The P-waves of the dominant morphology were further analyzed using wavelet transform whereas time domain characteristics were also extracted. Following a feature selection step, a SVM classifier was trained, for the discrimination of the PAF patients from the healthy subjects, while its accuracy was tested using the independent testing dataset.
In the cohort study, in both groups, the majority of the P-waves matched a main and a secondary morphology, while other morphologies were also present. The percentage of P-waves which simultaneously matched the main morphology in all three leads was lower in PAF patients (90.4 ± 7.8%) than in healthy subjects (95.5 ± 3.4%, p= 0.019). Three optimal scale bands were found and wavelet parameters were extracted which presented statistically significant differences between the two groups. Classification between the two groups was based on a feature selection process which highlighted 7 features, while an SVM classifier resulted a balanced accuracy equal to 93.75%. The results show the virtue of beat-to-beat analysis for PAF prediction.
The difference in the percentage of the main P-wave-morphology and in the P-wave time-frequency characteristics suggests a higher electrical instability of the atrial substrate in patients with PAF and different conduction patterns in the atria.
心房颤动（AF）是最常见的心律失常。房颤的起始和持续与心房重构现象有关，心房重构是指心房电特性和结构特性的改变。P波形态分析可以揭示心房基质上电活动传播的相关信息。本研究的目的是调查阵发性房颤（PAF）患者可能出现的P波形态模式，这些模式可作为区分PAF患者和健康受试者的依据。
在窦性心律（SR）期间，分析了时长3 - 5分钟的三个正交轴（X、Y和Z）上的矢量心电图信号。共有29例PAF患者和34名健康志愿者纳入分析。这些数据被分为两个不同的数据集，一个用于所提出方法的训练，另一个用于测试。该方法基于通过结合形态学的自适应k均值聚类和逐搏互相关技术来识别主要和次要P波形态。对主要形态的P波进一步使用小波变换进行分析，并提取时域特征。经过特征选择步骤后，训练了一个支持向量机（SVM）分类器，用于区分PAF患者和健康受试者，同时使用独立测试数据集测试其准确性。
在队列研究中，两组中大多数P波都匹配一种主要和一种次要形态，同时也存在其他形态。PAF患者中在所有三个导联中同时匹配主要形态的P波百分比（90.4±7.8%）低于健康受试者（95.5±3.4%，p = 0.019）。发现了三个最佳尺度带，并提取了小波参数，两组之间这些参数存在统计学显著差异。两组之间的分类基于一个突出7个特征的特征选择过程，而一个SVM分类器的平衡准确率等于93.75%。结果显示了逐搏分析对PAF预测的优势。
主要P波形态百分比和P波时频特征的差异表明PAF患者心房基质的电不稳定性更高，且心房传导模式不同。
