  Objective: Imbalances of the electrolyte concentration levels in the body can
lead to catastrophic consequences, but accurate and accessible measurements
could improve patient outcomes. While blood tests provide accurate
measurements, they are invasive and the laboratory analysis can be slow or
inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool
which is quick and simple to acquire. However, the problem of estimating
continuous electrolyte concentrations directly from ECGs is not well-studied.
We therefore investigate if regression methods can be used for accurate
ECG-based prediction of electrolyte concentrations. Methods: We explore the use
of deep neural networks (DNNs) for this task. We analyze the regression
performance across four electrolytes, utilizing a novel dataset containing over
290000 ECGs. For improved understanding, we also study the full spectrum from
continuous predictions to binary classification of extreme concentration
levels. To enhance clinical usefulness, we finally extend to a probabilistic
regression approach and evaluate different uncertainty estimates. Results: We
find that the performance varies significantly between different electrolytes,
which is clinically justified in the interplay of electrolytes and their
manifestation in the ECG. We also compare the regression accuracy with that of
traditional machine learning models, demonstrating superior performance of
DNNs. Conclusion: Discretization can lead to good classification performance,
but does not help solve the original problem of predicting continuous
concentration levels. While probabilistic regression demonstrates potential
practical usefulness, the uncertainty estimates are not particularly
well-calibrated. Significance: Our study is a first step towards accurate and
reliable ECG-based prediction of electrolyte concentration levels.
