Wavelet analysis of high-resolution ECGs in post-infarction patients: role of the basic wavelet and of the analyzed lead.

Wavelet analysis provides a fruitful alternative to standard techniques for the detection of fractionated potentials in signal averaged high-resolution (SA-HR) ECGs. In this study, an attempt is made to optimize the discrimination of post infarction patients prone to ventricular tachycardia (VT), using wavelet analysis. Optimization is based on the choice of the ECG leads or lead combinations to be analyzed, and on the analyzing wavelet to be computed. A set of 40 post-infarction patients (20 patients with VT and 20 patients without any arrhythmia) is analyzed. Individual leads and lead combinations of the SA-HR ECGs are processed using a multiparametric algorithm, based on coherent detection of aligned local maxima of the wavelet transform. Seven basic wavelets are tested: the Morlet's wavelet, and the six first derivatives of a Gaussian function. The first derivative of a Gaussian function provides poor results, and is discarded. All other wavelets prove to perform equivalent classification. A vector magnitude computed from the wavelet transforms of the three SA-HR ECGs achieves better results than individual leads. An optimized risk stratification algorithm leads to 90% sensitivity and 100% specificity in the 40 patients learning set.