Estimation of Blood Calcium and Potassium Values from ECG Records
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sciendo
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Bio-signals, chronic kidney disease, ion concentration, machine learning, non-invasive diagnostic
Kaynak
Measurement Science Review
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
24
Sayı
5
