Heart rate turbulence in adults with repaired tetralogy of Fallot.

BACKGROUND: Tetralogy of Fallot (ToF) patients face an increased risk of sudden cardiac death late after repair. Heart rate turbulence (HRT) indices are well-known predictors of sudden cardiac death. We aimed to estimate whether HRT is impaired in repaired ToF patients compared to healthy controls and relate those HRT parameters to already recognized prognostic markers. METHODS: Continuous ECG recordings were performed in 19 patients late after ToF repair (36.3+/-12.4 years, 26.6+/-7.1 years after repair) and 20 age-matched healthy controls (40.8+/-8.1 years). Turbulence slope (TS) and onset (TO), frequency and time domain heart rate variability parameters and QRS duration were estimated. Volumes of the right (RV) and left ventricle (LV) and ejection fraction (EF) were assessed by cardiovascular magnetic resonance imaging. Cardiopulmonary exercise testing was used to estimate peak oxygen consumption (VO(2)) and VE/VCO(2) slope. RESULTS: TS (15.95+/-9.41 vs 28.73+/-12.24 ms/RRI, p=0.0007) and TO (-0.98+/-2.06% vs -3.45+/-3.25%, p=0.007) were found to be significantly different between ToF patients and controls. TO correlated with LVEF (r=0.47, p<0.05), LVSVi (r=0.50, p=0.03), RVEF (r=0.53, p=0.02), peak VO(2) (r=0.50, p=0.05), VE/VCO(2) slope (r=-0.55, p=0.03) and with heart rate variability frequency domain indices (log LF, r=0.47, p=0.04, log HF, r=0.56, p=0.01). CONCLUSION: HRT indices are impaired in ToF patients late after surgical repair compared to healthy controls and relate to coexisting haemodynamic, ventilatory and autonomic impairment. A clinical prognostic role of HRT may be speculated, which warrants further investigation.

Assessment of the classification capability of prediction and approximation methods for HRV analysis.

The goal of this paper is to examine the classification capabilities of various prediction and approximation methods and suggest which are most likely to be suitable for the clinical setting. Various prediction and approximation methods are applied in order to detect and extract those which provide the better differentiation between control and patient data, as well as members of different age groups. The prediction methods are local linear prediction, local exponential prediction, the delay times method, autoregressive prediction and neural networks. Approximation is computed with local linear approximation, least squares approximation, neural networks and the wavelet transform. These methods are chosen since each has a different physical basis and thus extracts and uses time series information in a different way.