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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) reflectsΔ  [   K  +   ]and two time-warping-based T-wave morphological parameters,d  wand its heart-rate corrected versiond   w  ,  c, have been shown to reliably trackΔ  [   K  +   ]from the ECG. The aim of this study is to derive polynomial models relatingd  wandd   w  ,  cwithΔ  [   K  +   ], and to test their ability to reliably sense and quantifyΔ  [   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  wandd   w  ,  cwere 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) betweenΔ  [   K  +   ]and the corresponding model-estimated values (Δ  ^    [   K  +   ]) were calculated. Results and Discussions: Nonlinear models were the most suitable forΔ  [   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’sρwere found across models (median 0.77≤  ρ  ≤0.83). Conclusion: Results support the use of nonlinear T-wave-based models asΔ  [   K  +   ]sensors in ESRD-HD patients.

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