109510 20240209144726.0 doi 10.1016/j.bspc.2021.102719 sideral 125677 ART-2021-125677 eng Palmieri, Flavio ECG-based monitoring of blood potassium concentration: Periodic versus principal component as lead transformation for biomarker robustness 2021 Access copy available to the general public Unrestricted 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. © 2021 The Author(s) info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group info:eu-repo/grantAgreement/EUR/ERC-2014-StG-638284 info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-105674RB-I00 info:eu-repo/grantAgreement/ES/MICINN/PID2019-104881RB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 5.076 2021 ENGINEERING, BIOMEDICAL 30 / 98 = 0.306 2021 Q2 T1 1.211 2021 Signal Processing 2021 Q1 Health Informatics 2021 Q1 6.9 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Gomis, Pedro Ruiz, José Esteban Ferreira, Dina Martín-Yebra, Alba (orcid)0000-0003-0226-4950 Pueyo, Esther Universidad de Zaragoza (orcid)0000-0002-1960-407X Martínez, Juan Pablo Universidad de Zaragoza (orcid)0000-0002-7503-3339 Ramírez, Julia (orcid)0000-0003-4130-5866 Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 68 (2021), 102719 [11 pp.] Biomed. signal proces. control Biomedical Signal Processing and Control 1746-8094 3992167 http://zaguan.unizar.es/record/109510/files/texto_completo.pdf Versión publicada 2426403 http://zaguan.unizar.es/record/109510/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:109510 articulos driver 2024-02-09-14:45:41 ARTICLE