| Sumario: | 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 repre- senting 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 methodol- ogy 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 variabil- ity, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death.
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