Assessing real-time RR-QT frequency-domain measures of coupling and causality through inhomogeneous point-process bivariate models.

Abstract

Ventricular repolarization instability is known to be related to arrhythmogenesis and increased risk of sudden cardiac death. These repolarization dynamics are linked to the distance between T-wave and Q-wave occurrences (QT) on the ECG, and they are coupled with R-wave to R-wave interval variability (RRV). Several efforts have been dedicated to the analysis of QT-RR interactions in order to provide both a quantification of the coupling and estimates of intrinsic repolarization dynamics. However, a methodology able to quantify dynamic changes in repolarization variability unrelated to RRV dynamics is still needed. In this study, we propose a bivariate model embedded within a multiple inhomogeneous point-process framework to obtain time-varying tracking of (causal) interactions between QT variability (QTV), a marker of repolarization variability, and RRV. Data from 15 healthy subjects undergoing a tilt table test were analyzed. Our results demonstrate that the model effectively captures the time-varying mutual QTV-RRV interactions. The analysis of time-varying coherence confirms that head-up tilt is associated with a decrease in linear QTV-RRV coupling, while time-varying directed coherence shows that intrinsic QTV becomes more prominent during head-up tilt.