Key result
An ECG-based fuzzy inference system estimates blood calcium values with an R of ~0.57.
Why the study?
Diagnosing electrolyte concentration imbalances currently relies on blood tests, creating a need for non-invasive diagnostic methods that enable permanent home monitoring via bio-signals such as the ECG.
Can machine learning models using ECG features accurately estimate blood calcium and potassium levels?
Observational
No
Can machine learning models using ECG features accurately estimate blood calcium and potassium levels?
Machine learning models, particularly fuzzy inference systems, can estimate blood calcium levels from non-invasive ECG recordings, offering a potential tool for continuous electrolyte monitoring.
Hypothesis-generating for ECG-based calcium estimation; leaves open clinical validation and utility.
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.
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Babur et al. (2024) conducted an observational in Electrolyte imbalance (Calcium and Potassium). Fuzzy Inference Systems (FIS) and Neural Network Regression based on ECG features vs. Actual blood laboratory values was evaluated on Estimation of blood calcium value (Mean Square Error and R) (MSE 0.7157, R 0.57347). A fuzzy inference system using ECG recordings estimated blood calcium values with a mean square error of 0.7157 and an R of 0.57347 on the validation set.
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