Assessing instantaneous QT variability dynamics within a point-process nonlinear framework

The importance of cardiac repolarization dynamics in promoting arrhythmic events is widely recognized. To this extent, mathematical modeling and signal processing have played an important role in providing effective measures related to cardiac and autonomic nervous system dynamics. In this study, we introduce an instantaneous assessment of QT variability indices using a point-process nonlinear framework. The analysis includes computation of the dynamical spectrum and bispectrum, as well as time domain features, from data gathered from healthy subjects undergoing a tilt test trial. We demonstrate that an inverse-Gaussian probability function effectively predicts the current QT interval given a nonlinear combination of the past QT intervals modeled through the Laguerre expansion of the Wiener-Volterra terms. Results also show that our approach is able to provide an accurate instantaneous characterization of ventricular repolarization dynamics during changes with posture.