Background Wavelet decomposition of the signal-averaged electrocardiogram has been proposed as a method of detecting small and transient irregularities hidden within the QRS complex and of overcoming some of the limitations of time domain analysis of the signal-averaged electrocardiogram.
Aim This study evaluated the potential utility of wavelet decomposition analysis in the risk stratification of patients with idiopathic dilated cardiomyopathy.
Methods and Results Both wavelet decomposition and time domain analysis were applied to the signal-averaged electrocardiogram recordings of 82 patients with idiopathic dilated cardiomyopathy (mean age 43±14 years, 60 men) and 72 normal controls (mean age 44±15 years, 48 men). Three conventional time domain indices and four wavelet decomposition analysis parameters (QRS length, maximum count, surface area, and relative length) were derived from each recording using a Del Mar CEWS system and an in-house software package, respectively. The results showed that (1) more patients with idiopathic dilated cardiomyopathy than without had late potentials, and that the filtered QRS duration was significantly longer in patients than in controls (P<0·001). Similarly, abnormal wavelet decomposition analysis was more common in patients and wavelet decomposition measurements were significantly different between patients and controls (P<0·01); (2) conventional time domain analysis did not distinguish between clinically stable patients and patients who developed progressive heart failure, or between patients with and without arrhythmic events; (3) wavelet decomposition analysis identified patients who went on to develop progressive heart failure but failed to distinguish patients with arrhythmic events from those without; (4) survival analyses of a mean follow-up of 23 months showed that patients with late potentials tended to develop progressive heart failure more frequently than others (P=0·06). Patients with an abnormal wavelet decomposition result more frequently developed progressive heart failure than those with a normal wavelet decomposition result (P=0·027); (5) in a univariate analysis (Cox model), wavelet decomposition measurements but not time domain indices significantly correlated with the development of progressive heart failure (P=0·01). Multivariate analysis showed that only left ventricular end-diastolic dimension and peak oxygen consumption during exercise remained significant predictors of progressive heart failure.
Conclusion Wavelet decomposition analysis of the signal-averaged electrocardiogram is superior to conventional time domain analysis for identifying patients with idiopathic dilated cardiomyopathy at increased risk of clinical deterioration. Wavelet decomposition analysis, however, is unlikely to prospectively distinguish patients at a high risk of arrhythmic events in idiopathic dilated cardiomyopathy in its present form.
Wavelet decomposition, signal averaging, idiopathic dilated cardiomyopathy