ECG-based monitoring of blood potassium concentration: Periodic versus principal component as lead transformation for biomarker robustness

Abstract Objective 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. Methods 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). Results 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. Conclusion 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. Significance 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.