Time-scale analysis of high-resolution signal-averaged surface ECG using wavelet transformation

The authors investigate the contribution to the prediction of ventricular tachycardia (VT) of a new time-scale technique suited to transient signal detection: wavelet analysis (WA). Wavelet Transformation (WT) is obtained by expanding the signal on a set of functions resulting from the translation (time) and the dilatation (scale) of a so-called `analyzing wavelet´. It provides a bidimensional representation of the signal in function of time and scale. Bipolar X, Y, Z signals were acquired using a standard, commercially available signal-averaging system, in groups of 10 patients: myocardial infarction (MI) with VT, MI without VT, and healthy subjects. WT was based on J. Morlet wavelets. Contour maps allowed the localization of short, low energy transient signals, even within the QRS complex. Patients with VT displayed the most disturbed contour plots. It is concluded that WA, combined with an adequate graphic representation, provides an accurate characterization of patients prone to VT. These preliminary results are very promising as regards the potential improvements which may be obtained by searching for an optimal analyzing wavelet