Characterization of T Wave Amplitude, Duration and Morphology Changes During Hemodialysis: Relationship With Serum Electrolyte Levels and Heart Rate

Bukhari, Hassaan; Palmieri, Flavio; Ramírez, Julia; Laguna, Pablo; Ruiz, J.; Ferreira, Dina María Martins; Potse, Mark; Sánchez, Carlos; Pueyo, Esther

doi:10.1109/tbme.2020.3043844

“…For comparison purposes, both T w 14 and T S/A 15 were extracted from each MWTW and their performance, with respect to T-wave time-warping based biomarkers in monitoring [K + ] , was assessed. This work perform a clinical study following previous analysis testing the marker by electrophysiological simulations as reported in Bukhari et al 20 .…”

Section: Ecg Waveform Detection and Delineationmentioning

confidence: 99%

“…The explanation for this discrepancy could be tracked back to the biomarkers the authors took as reference for their research, from Dillon et al 13 and Corsi et al 15 , which are a) focused on specific features of the T-waves and b) measured in absolute value and not in relative terms to a reference which can personalize the biomarker as here presented. Nevertheless, time warping analysis was applied in recent studies 19,20 to investigate both hypo-and hyperkalemia on a extremely heterogeneous pool of simulated cases, performing equally well in all the cases and proving its adaptability, at least in silico simulated ECGs, probably due to its personalize profile as being comparative with a reference. Therefore, we hypothesized that the proposed methodology for [K + ] monitoring could be applied to investigate a wide range of populations and experimental sets.…”

Section: Spearman's ( ρ)mentioning

confidence: 99%

“…Therefore, we have adapted the original methodology 17 to account for hypo-and hyperkalemia, and we propose a new marker that is independent of HR, thus offering a more precise [K + ] monitoring tool for arrhythmic risk stratification in ESRD-HD patients. Preliminary results extracted from a smaller subset of patients have been presented at Computing in Cardiology conference 18,19 while the electrophysiological basis was studied in Bukhari et al 20 .…”

mentioning

confidence: 99%

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Monitoring blood potassium concentration in hemodialysis patients by quantifying T-wave morphology dynamics

Palmieri

¹

,

Gomis

²

,

Ferreira³

et al. 2021

Sci Rep

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We investigated the ability of time-warping-based ECG-derived markers of T-wave morphology changes in time ($$d_{w}$$ d w ) and amplitude ($$d_a$$ d a ), as well as their non-linear components ($${d_w^{{\mathrm{NL}}}}$$ d w NL and $${d_a^{\mathrm{NL}}}$$ d a NL ), and the heart rate corrected counterpart ($$d_{w,c}$$ d w , c ), to monitor potassium concentration ($$[K^{+}]$$ [ K + ] ) changes ($$\Delta [K^+]$$ Δ [ K + ] ) in end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). We compared the performance of the proposed time-warping markers, together with other previously proposed $$[K^{+}]$$ [ K + ] markers, such as T-wave width ($$T_w$$ T w ) and T-wave slope-to-amplitude ratio ($$T_{S/A}$$ T S / A ), when computed from standard ECG leads as well as from principal component analysis (PCA)-based leads. 48-hour ECG recordings and a set of hourly-collected blood samples from 29 ESRD-HD patients were acquired. Values of $$d_w$$ d w , $$d_a$$ d a , $${d_w^{\mathrm{NL}}}$$ d w NL , $${d_a^{\mathrm{NL}}}$$ d a NL and $$d_{w,c}$$ d w , c were calculated by comparing the morphology of the mean warped T-waves (MWTWs) derived at each hour along the HD with that from a reference MWTW, measured at the end of the HD. From the same MWTWs $$T_w$$ T w and $$T_{S/A}$$ T S / A were also extracted. Similarly, $$\Delta [K^+]$$ Δ [ K + ] was calculated as the difference between the $$[K^{+}]$$ [ K + ] values at each hour and the $$[K^{+}]$$ [ K + ] reference level at the end of the HD session. We found that $$d_{w}$$ d w and $$d_{w,c}$$ d w , c showed higher correlation coefficients with $$\Delta [K^+]$$ Δ [ K + ] than $$T_{S/A}$$ T S / A —Spearman’s ($$\rho$$ ρ ) and Pearson’s (r)—and $$T_w$$ T w —Spearman’s ($$\rho$$ ρ )—in both SL and PCA approaches being the intra-patient median $$\rho \ge 0.82$$ ρ ≥ 0.82 and $$r \ge 0.87$$ r ≥ 0.87 in SL and $$\rho \ge 0.82$$ ρ ≥ 0.82 and $$r \ge 0.89$$ r ≥ 0.89 in PCA respectively. Our findings would point at $$d_{w}$$ d w and $$d_{w,c}$$ d w , c as the most suitable surrogate of $$\Delta [K^+]$$ Δ [ K + ] , suggesting that they could be potentially useful for non-invasive monitoring of ESRD-HD patients in hospital, as well as in ambulatory settings. Therefore, the tracking of T-wave morphology variations by means of time-warping analysis could improve continuous and remote $$[K^{+}]$$ [ K + ] monitoring of ESRD-HD patients and flagging risk of $$[K^{+}]$$ [ K + ] -related cardiovascular events.

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“…Modeling Challenge References healthy heart-QRS modeling the Purkinje tree [1][2][3][4][5][6][7][8][9] healthy heart-T-wave modeling heterogeneity of repolarization [10][11][12][13] healthy heart-P-wave modeling sinus node excitation and pathways from right to left atrium, anatomical variability [14][15][16][17][18][19][20][21][22] ischemia and infarction modeling the effect of hyperkalemia, acidosis, hypoxia and cell-to-cell uncoupling [23][24][25][26][27][28][29][30] ventricular ectopic beats localization with 12-lead ECG [31][32][33][34] ventricular tachycardia localization of exit points with 12-lead ECG [29,35] cardiomyopathy modeling typical changes of QRS-and T-wave [36] bundle branch blocks LBBB and RBBB modeling asynchrony [37][38][39][40] atrial ectopic beats localization with 12-lead ECG [32][33][34] atrial tachycardia, flutter modeling all types of flutter…”

Section: Topicmentioning

confidence: 99%

Computer Modeling of the Heart for ECG Interpretation—A Review

Dössel

¹

,

Luongo

²

,

Nagel

³

et al. 2021

Hearts

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Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.

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“…Several studies in the literature have attempted to estimate [K + ] through the analysis of T-wave morphology changes, quantified by features representative of the T-wave shapes, [15][16][17]. In previous studies [18][19][20][21][22], we proposed and investigated six T-wave morphological parameters quantifying T-wave morphology changes by means of time warping analysis [23] for continuous non-invasive ∆[K + ] monitoring. These six T-wave morphology parameters included d u w , d w , andd w,c (unsigned, signed, and heart rate corrected T-wave morphology variations in time, respectively), d a (T-wave morphology variations in amplitude), and their non-linear components (d NL w and d NL a ) as described in [21,23].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study

Palmieri

¹

,

Gomis

²

,

Ruiz

³

et al. 2021

Sensors

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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, dw and its heart-rate corrected version dw,c, have been shown to reliably track Δ[K+] from the ECG. The aim of this study is to derive polynomial models relating dw and dw,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, dw and dw,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 (ed) 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 ≤ed≤ 0.43) than the linear model (median 0.76 ≤r≤ 0.86 and 0.30 ≤ed≤ 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.

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Characterization of T Wave Amplitude, Duration and Morphology Changes During Hemodialysis: Relationship With Serum Electrolyte Levels and Heart Rate

Cited by 13 publications

References 55 publications

Monitoring blood potassium concentration in hemodialysis patients by quantifying T-wave morphology dynamics

Monitoring blood potassium concentration in hemodialysis patients by quantifying T-wave morphology dynamics

Computer Modeling of the Heart for ECG Interpretation—A Review

Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study

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