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

José Esteban Ruiz,Flavio Palmieri,Esther Pueyo,Pablo Laguna,Dina Ferreira,Julia Ramírez,Pedro Gomis,Juan Pablo Martínez,Alba Martín-Yebra

doi:10.1016/j.bspc.2021.102719

Abstract

ObjectiveThe 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. MethodsWe 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). ResultsThe 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. ConclusionBoth 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. SignificanceThe 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.

Highlights

Continuous cardiac monitoring using electrocardiogram (ECG) re cordings has become increasingly important for early detection of car diovascular risk [1,2,3]
These T-wave descriptors were obtained by applying principal component analysis (PCA) as a lead space reduction (LSR) technique, so that morphology analysis is performed over the transformed lead (TL) maximizing the T-wave variance
To perform a thorough and comprehensive evaluation of the proposed time-warping markers, we investigated three different specific and supervised scenarios: (i) [K+] induced variations with no concurrent body position changes (BPC) (ESRD-HD dataset during HD), (ii) controlled BPC with no concurrent [K+] variations (BPC dataset) and (iii) simulated ECGs with three types of added noise at different signal-to-noise ratio (SNR) values simulating [K+]-driven T wave induced variations but without BPC (ECG simulation dataset)

Summary

Introduction

Continuous cardiac monitoring using electrocardiogram (ECG) re cordings has become increasingly important for early detection of car diovascular risk [1,2,3]. We proposed and investigated the feasibility of several T-wave morphology-based markers derived from T-wave time-warping analysis [15] in tracking [K+] [16,17,18,19] These T-wave descriptors were obtained by applying principal component analysis (PCA) as a lead space reduction (LSR) technique, so that morphology analysis is performed over the transformed lead (TL) maximizing the T-wave variance. An alternative LSR technique to PCA is periodic component analysis (πCA) [22,23], which transforms the multi-lead ECG signal by maxi mizing the periodic components on the TL This technique has already been applied to the ECG to detect T-wave alternans [24], demonstrating superior performance to PCA in noisy scenarios

Methods

Results

Discussion

Conclusion