A Time-Varying Non-Parametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability

OBJECTIVE: To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in non-stationary conditions. METHODS: Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the non-stationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging non-stationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed. RESULTS: In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the non-stationary spectra. The correlation coefficient between theoretical and estimated patterns was >0.92 even for extremely noisy recordings (SNR in QTV =-10dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands (P<0.05 for most pairwise comparisons). CONCLUSION: The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV. SIGNIFICANCE: The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death

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

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 occurrances (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