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.

[1]  E. Brown,et al.  A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. , 2005, American journal of physiology. Heart and circulatory physiology.

[2]  L T Mainardi,et al.  Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time–frequency analysis , 2012, Physiological measurement.

[3]  V. Yeragani,et al.  Nonlinear measures of QT interval series: novel indices of cardiac repolarization lability: MEDqthr and LLEqthr , 2003, Psychiatry Research.

[4]  Mathias Baumert,et al.  Relation between QT interval variability and cardiac sympathetic activity in hypertension. , 2011, American journal of physiology. Heart and circulatory physiology.

[5]  Richard Balon,et al.  Effect of Posture and Isoproterenol on Beat-to-Beat Heart Rate and QT Variability , 2000, Neuropsychobiology.

[6]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[7]  Luca Citi,et al.  Instantaneous estimation of high-order nonlinear heartbeat dynamics by Lyapunov exponents , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Pablo Laguna,et al.  QT variability and HRV interactions in ECG: quantification and reliability , 2006, IEEE Transactions on Biomedical Engineering.

[9]  Pablo Laguna,et al.  A multivariate time-frequency method to characterize the influence of respiration over heart period and arterial pressure , 2012, EURASIP J. Adv. Signal Process..

[10]  A. Malliani,et al.  Quantifying electrocardiogram RT-RR variability interactions , 2006, Medical and Biological Engineering and Computing.

[11]  Sandy Berger,et al.  Acute psychosis leads to increased QT variability in patients suffering from schizophrenia , 2007, Schizophrenia Research.

[12]  Luca Citi,et al.  Using Laguerre expansion within point-process models of heartbeat dynamics: A comparative study , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  G. Valenza,et al.  Tetravariate point-process model for the continuous characterization of cardiovascular-respiratory dynamics during passive postural changes , 2012, 2012 Computing in Cardiology.

[14]  H. Calkins,et al.  Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. , 1997, Circulation.

[15]  Luca Citi,et al.  Instantaneous nonlinear assessment of complex cardiovascular dynamics by laguerre-volterra point process models , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Luca Citi,et al.  Point-Process Nonlinear Models With Laguerre and Volterra Expansions: Instantaneous Assessment of Heartbeat Dynamics , 2013, IEEE Transactions on Signal Processing.