Centre for Wireless Monitoring Applications, Griffith University, Brisbane, 4111, Queensland, Australia.
Comput Methods Programs Biomed. 2011 Jan;101(1):33-43. doi: 10.1016/j.cmpb.2010.04.012. Epub 2010 May 26.
P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous wavelet transform (CWT) which are shown to be potentially effective discriminators in an automated diagnostic process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These wavelet models were also compared to standard cardiological measures of duration, terminal force and duration divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the present study.
心电图（ECG）中的 P 波特征是诊断心房传导病变的重要信息来源。然而，通过视觉检查进行诊断是一项艰巨的任务，因为 P 波相对较小，并且经常存在噪声掩蔽。本文介绍了从连续小波变换（CWT）中得出的新的小波特征，这些特征在自动化诊断过程中被证明是潜在有效的判别器。使用 CWT 和统计方法得出 12 导联心电图 P 波的特征。比较了正常对照组和异常组（心房传导病变）。小波特征捕获了 P 波的频率、幅度和方差分量。选择最佳的个体特征（即能显著区分两组的特征），并将其输入到线性判别分析（LDA）中，以构建四个不同的模型：双导联心电图、三导联心电图、衍生的三导联心电图和由小波特征在因子上的负荷组成的因子分析解决方案。还比较了从每个参与者的单个 P 波得出的小波特征与从信号平均 P 波得出的小波特征。还将这些小波模型与持续时间、终末力和 PR 段划分的持续时间等标准心脏病学测量值进行了比较。基于分类性能和简单性，个体 P 波方法的结果通常优于标准心脏病学测量值和信号平均 P 波方法。最佳的小波模型是基于导联 II 和 V1 的双导联模型。结论是，使用更大的样本验证和扩展本研究，值得进一步研究自动分类的小波方法。
