Identification of post-myocardial infarction patients prone to ventricular tachycardia using time-frequency analysis of QRS and ST segments.

BACKGROUND

Late potentials (LPs) in the terminal portion of the QRS complex are commonly sought to identify post-myocardial infarction patients prone to ventricular tachyarrthythmias (VT) or sudden death. More recent time frequency signal processing tools have been shown to provide new parameters for the quantification of LPs and abnormal activities buried within the QRS complex.

METHODS AND RESULTS

The study population comprised 23 myocardial infarction patients with documented sustained VT (MI+VT), 40 myocardial infarction patients without VT (MI - VT) and 31 normal subjects. The reproducibility of the method was tested in an additional set of 66 patients. The signal-averaged high-resolution electrocardiograms (HRECGs) were quantified by deconstructing the unfiltered X, Y and Z leads using a 511-orthogonal wavelet network. Using receiver operating characteristics (ROC) curves and discriminant analysis applied to the wavelet coefficients, we extracted the most significant wavelets to classify the post MI patients. These wavelets detected time-frequency alterations both in the ST segment and within the QRS complex, characterizing patients prone to VTs. The same statistical methods were applied to the conventional time-domain measurements. The combined application in our population of the orthogonal wavelet deconstruction method and discriminant analysis had 91% sensitivity and 95% specificity, an improvement of 22% and 25%, respectively, compared with the conventional time domain method. Reproducibility was 82%.

CONCLUSIONS

In post-myocardial infarction patients, orthogonal wavelet transforms can detect alterations in high-frequency components within the QRS and ST segment. Our findings support the view that wavelet-related parameters are more relevant than those of the time domain method in predicting subsequent malignant tachyarrhythmias.