QRS Complex Wavelet Analysis Can Distinguish Patients with and without Heart Failure, in the Presence of Left Bundle Branch Block

Background: Left bundle branch block (LBBB) in heart failure (HF) patients is a negative predictor of survival. This pattern is occasionally recorded in individuals without structural heart disease. The LBBB morphology has not been previously analyzed in a time-frequency domain using wavelet analysis), and thus the factors distinguish LBBB patients from individuals without structural heart disease remain unexplored. The purpose of this analysis was to investigate the variations and the differences in LBBB morphology between healthy individuals with LBBB and patients with HF and LBBB. Methods: HF patients with LBBB and individuals with LBBB were included in this study. Signal-averaged 90-second Holter monitor recordings were extracted from each subject in orthogonal leads. QRS decomposition in 9 time-frequency bands (TFB) was performed using Complex Morlet wavelets transformation, while the mean and maximum energies of the QRS complexes were calculated for each of the 9 TFBs. The wavelet parameters of HF patients were compared with those of healthy controls. Results: Wavelet analysis was performed on ECG recordings of 69 HF patients and 17 individuals without cardiac disease. The mean and max wavelet energies of the QRS complex in all TFBs were higher for heart failure patients with LBBB, as compared to healthy individuals with LBBB. Differences were statistically significant in TFB4 and TFB7 (max energy, axis X), TFB4 and TFB7 (max energy, axis Y) and TFB4 and TFB7 (mean energy, axis Y). A multivariate logistic regression model, comprising of the aforementioned wavelet parameters, proved reasonably capable of distinguishing between HF patients and healthy controls with LBBB (AUC=0.854, 80.2% sensitivity and 80.3% specificity). Conclusion: QRS wavelet analysis revealed differences in the template of the QRS complex between healthy individuals with LBBB and heart failure patients with LBBB. This feature could be used as part of the diagnostic algorithm, a possibility that should be investigated further.