WAVELET ANALYSIS OF HIGH-RESOLUTION SIGNAL-AVERAGED ECGS IN POSTINFARCTION PATIENTS

The authors present an original method for the discrimination of patients prone to ventricular tachycardia. The wavelet transform, which is a new time-scale technique suitable for transient signal detection, was applied to bipolar unfiltered X, Y, Z signal-averaged electrocardiograms in 20 postinfarction patients with sustained ventricular tachycardia, in 20 myocardial infarction patients without ventricular tachycardia, and in 10 healthy subjects. An improved automated algorithm for the detection and localization of sharp variations of the signal, based on coherent detection of the local maxima of the wavelet transform, was developed. A risk stratification method, based on the detection of at least one singularity at or after a point defined with reference to the QRS onset, was assessed. The optimum cutoff point, found 98 ms after the onset of QRS, provides a specificity of 90% and a sensitivity of 85 %. The authors conclude that wavelet analysis makes it possible, in this group of patients, to discriminate those with ventricular tachycardia. It yields better results than those obtained from the conventional time-domain approach.