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Background: T-wave (TW) morphology indices based on time-warping ($d_{w}$) have shown significant cardiovascular risk stratification value. However, errors in the location of TW boundaries may impact their prognostic power. Our aim was to test the hypothesis that a weighted time-warping function (WF) would reduce the sensitivity of $d_{w}$ to these errors and improve their clinical significance. Methods: The WFs were proportional to (i) the reference TW ($\mathcal {T}$), and (ii) the absolute value of its derivative ($\mathcal {D}$). The index $d_{w}$ was recalculated using these WFs, and its performance was compared to the unweighted control case ($\mathcal {C}$) in four different scenarios: 1) robustness against simulated TW boundaries location errors; 2) ability to retain physiological information in an electrophysiological cardiac model; 3) ability to monitor blood potassium concentration changes ($\Delta [K^+]$) in 29 hemodialysis (HD) patients; 4) and the sudden cardiac death (SCD) risk stratification value of the TW morphology restitution (TMR) index, derived from $d_{w}$ , in 651 chronic heart failure (CHF) patients. Results and Discussion: The WFs led to a reduced sensitivity ($\mathcal {R}$) of $d_{w}$ to TW boundary location errors as compared to $\mathcal {C}$ (median $\mathcal {R}$ =0.19 and 0.22 and 0.35 for $\mathcal {T}$ , $\mathcal {D}$ and $\mathcal {C}$ , respectively). They also preserved the physiological relationship between $d_{w}$ and repolarization dispersion changes at ventricular level. No improvements in $\Delta [K^+]$ tracking were observed for the HD patients (Pearson’s median correlation [ $r$ ] between $\Delta [K^+]$ and $d_{w}$ was $0.86\leq r \leq 0.90$ for $\mathcal {T}$ , $\mathcal {D}$ and $\mathcal {C}$). In CHF patients, the SCD risk stratification value of TMR was improved by applying $\mathcal {T}$ (hazard ratio, HAR, of 2.80), followed by $\mathcal {D}$ (HAR=2.32) and $\mathcal {C}$ (HAR=2.23). Conclusions and Significance: The proposed WFs, with $\mathcal {T}$ showing the best performance, increased the robustness of time-warping based markers against TW location errors preserving their physiological information content and boosting their SCD risk stratification value. Results from this work support the use of $\mathcal {T}$ when deriving $d_{w}$ for future clinical applications. [ABSTRACT FROM AUTHOR]

The aim of this study is to compare the performance of two electrocardiogram (ECG) lead-space reduction (LSR) techniques in generating a transformed ECG lead from which T-wave morphology markers can be reliably derived to non-invasively monitor blood potassium concentration ([ K + ]) in end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). These LSR techniques are: (1) principal component analysis (PCA), learned on the T wave, and (2) periodic component analysis (π CA), either learned on the whole QRST complex (π C B) or on the T wave (π C T). We hypothesized π CA is less sensitive to non-periodic disturbances, like noise and body position changes (BPC), than PCA, thus leading to more reliable T wave morphology markers. We compared the ability of T wave morphology markers obtained from PCA, π C B and π C T in tracking [ K + ] in an ESRD-HD dataset, including 29 patients, during and after HD (evaluated by correlation and residual fitting error analysis). We also studied their robustness to BPC using an annotated database, including 20 healthy individuals, as well as to different levels of noise using a simulation set-up (assessed by means of Mann–Whitney U test and relative error, respectively). The performance of both π C B and π C T -based markers in following [ K + ] -variations during HD was comparable, and superior to that from PCA-based markers. Moreover, π C T -based markers showed superior robustness against BPC and noise. Both π C B and π C T outperform PCA in terms of monitoring [ K + ] in ESRD-HD patients, as well as of robustness against BPC and low SNR, with π C T showing the highest stability for continuous post-HD monitoring. The usage of π CA (i) increases the accuracy in monitoring dynamic [ K + ] variations in ESRD-HD patients and (ii) reduces the sensitivity to BPC and noise in deriving T wave morphology markers. [ABSTRACT FROM AUTHOR]

Background: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([ K + ]) variations ( Δ [ K + ] ) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [ K + ] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects Δ [ K + ] and two time-warping-based T-wave morphological parameters, d w and its heart-rate corrected version d w , c , have been shown to reliably track Δ [ K + ] from the ECG. The aim of this study is to derive polynomial models relating d w and d w , c with Δ [ K + ] , and to test their ability to reliably sense and quantify Δ [ K + ] values. Methods: 48-hour Holter ECGs and [ K + ] values from six blood samples were collected from 29 ESRD-HD patients. For every patient, d w and d w , c were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman's (ρ) and Pearson's (r) correlation coefficients, and the estimation error ( e d ) between Δ [ K + ] and the corresponding model-estimated values ( Δ ^ [ K + ] ) were calculated. Results and Discussions: Nonlinear models were the most suitable for Δ [ K + ] estimation, rendering higher Pearson's correlation (median 0.77 ≤ r ≤ 0.92) and smaller estimation error (median 0.20 ≤ e d ≤ 0.43) than the linear model (median 0.76 ≤ r ≤ 0.86 and 0.30 ≤ e d ≤ 0.40), even if similar Spearman's ρ were found across models (median 0.77 ≤ ρ ≤ 0.83). Conclusion: Results support the use of nonlinear T-wave-based models as Δ [ K + ] sensors in ESRD-HD patients. [ABSTRACT FROM AUTHOR]