Estimation of Blood Calcium and Potassium Values from ECG Records

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dc.contributor.authorBabur, Sebahattin
dc.contributor.authorMoghaddamnia, Sanam
dc.contributor.authorBozkurt, Mehmet Recep
dc.date.accessioned2025-02-20T08:42:14Z
dc.date.available2025-02-20T08:42:14Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractThe identification of diseases caused by changes in ion concentration is quite difficult and yet plays a decisive role in the success of clinical care, diagnosis and treatment. The clinically proven approach to diagnosing electrolyte concentration imbalance is blood tests. There is a need to provide a non-invasive diagnostic method that is not of a temporary nature. Bio-signals such as the electrocardiogram (ECG) can be used to meet this demand and become diagnostic tools that facilitate home monitoring of electrolyte concentration on a permanent basis. This study investigates the feasibility and efficiency of methods based on machine learning (ML) and ECG recordings in monitoring critical levels of existing potassium and calcium concentration. Morphological, frequency and frequency-time domain features were extracted to automatically estimate calcium and potassium levels. Furthermore, the potential of estimates based on modeling approaches will be demonstrated to gain insights into relevant clinical findings and improve the performance of monitoring approaches. Using the hold-out validation method, the best results in terms of mean square error (MSE) and R for estimating the calcium value are 0.7157 and 0.57347, using fuzzy inference systems (FIS). Here, R represents the proportion of the variance in the calcium value that is explained by the model.
dc.identifier.doi10.2478/msr-2024-0022
dc.identifier.endpage173en_US
dc.identifier.issn1335-8871
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85208667183
dc.identifier.scopusqualityQ3
dc.identifier.startpage158en_US
dc.identifier.urihttps://doi.org/10.2478/msr-2024-0022
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1580
dc.identifier.volume24en_US
dc.identifier.wosWOS:001346004300004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSciendo
dc.relation.ispartofMeasurement Science Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250220
dc.subjectBio-signalsen_US
dc.subjectchronic kidney diseaseen_US
dc.subjection concentrationen_US
dc.subjectmachine learningen_US
dc.subjectnon-invasive diagnosticen_US
dc.titleEstimation of Blood Calcium and Potassium Values from ECG Records
dc.typeArticle

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