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

dc.contributor.authorFlavio Palmieri
dc.contributor.authorPedro Gomis
dc.contributor.authorJosé Esteban Ruiz
dc.contributor.authorDina Ferreira
dc.contributor.authorAlba Martín-Yebra
dc.contributor.authorEsther Pueyo
dc.contributor.authorJuan Pablo Martínez
dc.contributor.authorJulia Ramírez
dc.contributor.authorPablo Laguna
dc.contributor.otherCentre de Recerca en Enginyeria Biomèdica, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
dc.contributor.otherCentre de Recerca en Enginyeria Biomèdica, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
dc.contributor.otherNephrology Ward, Hospital Clínico Universitario Lozano Blesa, 50009 Zaragoza, Spain
dc.contributor.otherLaboratorios Rubió, Castellbisbal, 08755 Barcelona, Spain
dc.contributor.otherCIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain
dc.contributor.otherCIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain
dc.contributor.otherCIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain
dc.contributor.otherWilliam Harvey Research Institute, Queen Mary University of London, London E1 4NS, UK
dc.contributor.otherCIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain
dc.date.accessioned2021-04-13T00:04:25Z
dc.date.available2025-10-02T03:47:34Z
dc.date.issued01-04-2021
dc.identifier.issn-
dc.identifier.urihttps://www.mdpi.com/1424-8220/21/8/2710
dc.description.abstractBackground: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>K</mi><mo>+</mo></msup></semantics></math></inline-formula>]) variations (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula>) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>K</mi><mo>+</mo></msup></semantics></math></inline-formula>] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula> and two time-warping-based T-wave morphological parameters, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>d</mi><mi>w</mi></msub></semantics></math></inline-formula> and its heart-rate corrected version <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>d</mi><mrow><mi>w</mi><mo>,</mo><mi>c</mi></mrow></msub></semantics></math></inline-formula>, have been shown to reliably track <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula> from the ECG. The aim of this study is to derive polynomial models relating <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>d</mi><mi>w</mi></msub></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>d</mi><mrow><mi>w</mi><mo>,</mo><mi>c</mi></mrow></msub></semantics></math></inline-formula> with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula>, and to test their ability to reliably sense and quantify <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula> values. Methods: 48-hour Holter ECGs and [<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>K</mi><mo>+</mo></msup></semantics></math></inline-formula>] values from six blood samples were collected from 29 ESRD-HD patients. For every patient, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>d</mi><mi>w</mi></msub></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>d</mi><mrow><mi>w</mi><mo>,</mo><mi>c</mi></mrow></msub></semantics></math></inline-formula> were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman’s (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula>) and Pearson’s (<i>r</i>) correlation coefficients, and the estimation error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>e</mi><mi>d</mi></msub></semantics></math></inline-formula>) between <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula> and the corresponding model-estimated values (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mi mathvariant="sans-serif">Δ</mi><mo stretchy="false">^</mo></mover><mrow><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></mrow></semantics></math></inline-formula>) were calculated. Results and Discussions: Nonlinear models were the most suitable for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula> estimation, rendering higher Pearson’s correlation (median 0.77 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≤</mo><mi>r</mi><mo>≤</mo></mrow></semantics></math></inline-formula> 0.92) and smaller estimation error (median 0.20 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≤</mo><msub><mi>e</mi><mi>d</mi></msub><mo>≤</mo></mrow></semantics></math></inline-formula> 0.43) than the linear model (median 0.76 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≤</mo><mi>r</mi><mo>≤</mo></mrow></semantics></math></inline-formula> 0.86 and 0.30 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≤</mo><msub><mi>e</mi><mi>d</mi></msub><mo>≤</mo></mrow></semantics></math></inline-formula> 0.40), even if similar Spearman’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula> were found across models (median 0.77 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>≤</mo><mi>ρ</mi><mo>≤</mo></mrow></semantics></math></inline-formula> 0.83). Conclusion: Results support the use of nonlinear T-wave-based models as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><mo>[</mo><msup><mi>K</mi><mo>+</mo></msup><mo>]</mo></mrow></semantics></math></inline-formula> sensors in ESRD-HD patients.
dc.format-
dc.language.isoEN
dc.publisherMDPI AG
dc.relation.uri['https://www.sciencedirect.com/journal/journal-of-otology', 'https://www.sciencedirect.com/journal/journal-of-otology/publish/guide-for-authors']
dc.rights['CC BY', 'CC BY-NC-ND']
dc.subject['hearing', 'balance', 'cochlea', 'otology', 'implant', 'genes', 'Otorhinolaryngology', 'RF1-547']
dc.subject.lccChemical technology
dc.titleNonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study
dc.typeArticle
dc.description.keywordselectrocardiogram
dc.description.keywordsperiodic component analysis
dc.description.keywordsT-wave morphology
dc.description.keywordstime warping
dc.description.keywordsnoninvasive potassium sensing
dc.description.keywordspersonalised medicine
dc.description.pages-
dc.description.doi10.3390/s21082710
dc.title.journalSensors
dc.identifier.e-issn1424-8220
dc.identifier.oaioai:doaj.org/journal:bdd5b370ece04cb3aaea32263cd0723a
dc.journal.infoVolume 21, Issue 8


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