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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. |
