Background: The T-wave (TW) morphology has been extensively investigated to develop specific markers of risk. However, TW boundaries delineation errors may jeopardise the diagnostic ability of TW-based morphological markers, like dw, which quantifies the level of warping needed to temporally align a studied TW with respect to a reference one. To reduce the impact of these errors on the dw calculation, we proposed and tested two TW-based weighting functions (WFs).
Materials and Methods: The two proposed WFs were (i) the reference TW (T), and (ii) the absolute value of its first-derivative (D). We, first, simulated TW boundaries delineation errors by shortening and widening two TWs with morphological variability. We, then, used the variation ratio (R) to compare dw derived applying the two WFs with that obtained in the control case (no weighting, C). Next, we compared the ability of dw, with and without WFs, in monitoring blood potassium concentration changes (Δ[K+]) by means of the Pearson's correlation (r) in 29 48-hour Holter recordings from hemodyalisis (HD) patients.
Results: The simulations showed that the R values for the two studied TWs, respectively, were 0.17 and 0.19 for C, 0.05 and 0.08 for T and 0.07 and 0.10 for D, indicating that both WFs reduce the effects of TW boundaries delineation errors. However, similar r median [interquartile range] values were found for C (0.90 [0.27]), T (0.90 [0.25]) and D (0.90 [0.29]) in the HD dataset, suggesting that the effects of TW boundaries delineation errors were small enough not to affect the dw ability for Δ[K+] monitoring.
Conclusions: Using WFs to compute dw reduces the effects of TW boundaries delineation errors, but no improvements in Δ[K+] monitoring in HD patients were observed. WFs impact on dw risk prediction value remains to be evaluated.