Couderc J P, Chevalier P, Fayn J, Rubel P, Touboul P
INSERM U121 and Division of Clinical Electrophysiology, H pital Cardiologique Louis Pradel, Lyon, France.
Europace. 2000 Apr;2(2):141-53. doi: 10.1053/eupc.2000.0091.
Late potentials (LPs) in the terminal portion of the QRS complex are commonly sought to identify post-myocardial infarction patients prone to ventricular tachyarrthythmias (VT) or sudden death. More recent time frequency signal processing tools have been shown to provide new parameters for the quantification of LPs and abnormal activities buried within the QRS complex.
The study population comprised 23 myocardial infarction patients with documented sustained VT (MI+VT), 40 myocardial infarction patients without VT (MI - VT) and 31 normal subjects. The reproducibility of the method was tested in an additional set of 66 patients. The signal-averaged high-resolution electrocardiograms (HRECGs) were quantified by deconstructing the unfiltered X, Y and Z leads using a 511-orthogonal wavelet network. Using receiver operating characteristics (ROC) curves and discriminant analysis applied to the wavelet coefficients, we extracted the most significant wavelets to classify the post MI patients. These wavelets detected time-frequency alterations both in the ST segment and within the QRS complex, characterizing patients prone to VTs. The same statistical methods were applied to the conventional time-domain measurements. The combined application in our population of the orthogonal wavelet deconstruction method and discriminant analysis had 91% sensitivity and 95% specificity, an improvement of 22% and 25%, respectively, compared with the conventional time domain method. Reproducibility was 82%.
In post-myocardial infarction patients, orthogonal wavelet transforms can detect alterations in high-frequency components within the QRS and ST segment. Our findings support the view that wavelet-related parameters are more relevant than those of the time domain method in predicting subsequent malignant tachyarrhythmias.
通常通过寻找QRS波群终末部分的晚电位（LPs）来识别心肌梗死后易于发生室性心律失常（VT）或猝死的患者。最近的时频信号处理工具已被证明可为LPs及QRS波群中隐藏的异常活动的量化提供新参数。
研究人群包括23例有持续性VT记录的心肌梗死患者（MI+VT）、40例无VT的心肌梗死患者（MI - VT）和31名正常受试者。该方法的可重复性在另外66例患者中进行了测试。通过使用511正交小波网络解构未滤波的X、Y和Z导联对信号平均高分辨率心电图（HRECGs）进行量化。利用应用于小波系数的受试者工作特征（ROC）曲线和判别分析，我们提取了最显著的小波来对心肌梗死后患者进行分类。这些小波检测到了ST段和QRS波群内的时频改变，从而对易于发生VT的患者进行了特征描述。相同的统计方法应用于传统时域测量。在我们的研究人群中，正交小波解构方法与判别分析的联合应用敏感性为91%，特异性为95%，与传统时域方法相比分别提高了22%和25%。可重复性为82%。
在心肌梗死后患者中，正交小波变换可检测QRS波群和ST段内高频成分的改变。我们的研究结果支持这样一种观点，即与小波相关的参数在预测随后的恶性心律失常方面比时域方法的参数更具相关性。
