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

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 (πCA), either learned on the whole QRST complex (πCB) or on the T wave (πCT). We hypothesized πCA 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, πCB and πCT 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 πCB and πCT-based markers in following [K]-variations during HD was comparable, and superior to that from PCA-based markers. Moreover, πCT-based markers showed superior robustness against BPC and noise. Conclusion: Both πCB and πCT outperform PCA in terms of monitoring [K] in ESRD-HD patients, as well as of robustness against BPC and low SNR, with πCT showing the highest stability for continuous post-HD monitoring. Significance: The usage of πCA (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.