Multiresolution decomposition of the signal-averaged ECG using the Mallat approach for prediction of arrhythmic events after myocardial infarction

The aim of this study was to analyze the ability of the multiresolution decomposition of the signal-averaged electrocardiogram (EGG) to discriminate between patients who develop life-threatening ventricular arrhythmias after myocardial infarction and those who do not and to compare the predictive values of this approach with those obtained from the analysis of ventricular late potentials in the time domain. Signal-averaged ECGs of 769 prospectively included patients were analyzed. A total of 42 arrhythmic events occurred during the follow-up period. For numerical calculations of wavelet analysis, the total and relative energies of the QRS complex were obtained in seven frequency bands. The combination of the relative energy in the frequency bands 7.8-15.6 Hz and 62.5-125 Hz enhanced statistical performance as compared with the time-domain parameters (positive predictive accuracy, 11.3 vs 8.2%). Combining wavelet transform and time-domain parameters enhanced the predictive values even more (positive predictive accuracy, 14.3%) compared with applying each method alone.