Potassium disorders are generally asymptomatic, potentially lethal, and common in patients with renal or cardiac disease. The morphology of the electrocardiogram (ECG) signal is very sensitive to the changes in potassium ions, so ECG has a high potential for detecting dyskalemias before laboratory results. In this regard, this paper introduces a new system for ECG-based potassium measurement. The proposed system consists of three main steps. First, cohort selection & data labeling were carried out by using a 5- minute interval between ECGs and potassium measurements and defining three labels: hypokalemia, normal, and hyperkalemia. After that, feature extraction & selection were performed. The extracted features are RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, T axis, and ACCI. Kruskal-Wallis technique was also used to assess the importance of the features and to select discriminative ones. Finally, an ANFIS model based on FCM clustering (FCM-ANFIS) was designed based on the selected features. The used database is ECG-ViEW II. Results showed that T axis compared with other features has a significant relationship with potassium levels (P<0.01, r=0.62). The absolute error of FCM-ANFIS is 0.4+-0.3 mM, its mean absolute percentage error (MAPE) is 9.99%, and its r-squared value is 0.74. Its classification accuracy is 85.71%. In detecting hypokalemia and hyperkalemia, the sensitivities are 60% and 80%, respectively, and the specificities are 100% and 97.3%, respectively. This research has shed light on the design of noninvasive instruments to measure potassium concentration and to detect dyskalemias, thereby reducing cardiac events.

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.

Renalytix AI plc announced a collaboration with AstraZeneca to develop and launch precision medicine strategies for cardiovascular, renal and metabolic diseases. The first stage in the collaboration will use KidneyIntelX, an artificial intelligence-enabled in vitro diagnostic platform, to examine further improving outcomes for patients with chronic kidney disease (CKD) and its complications, in coordination with the Mount Sinai Health System. The goal of the first stage is to help improve guideline-based standard-of-care for optimal utilization of existing and novel therapeutics using the KidneyIntelX testing platform and proprietary care management software. An estimated 700 million patients worldwide have CKD,1 which is also associated with an increased risk of metabolic and hematologic complications, such as hyperkalemia (elevated levels of potassium in the blood) and anemia.2,3 The first stage will assess the impact of AI-enabled in vitro diagnostic solutions to optimize utilization of therapeutics in CKD under current standard of care protocols.

High blood potassium levels constitute a condition known as hyperkalemia. It can be related to a number of causes, including kidney disease, dehydration, injury and diabetes and hyperkalemia can affect heartbeat rhythm. Yesterday, during the American College of Cardiology conference, AliveCor presented work done with the Mayo Clinic showing that its technology can detect hyperkalemia through EKGs. The researchers used electrocardiogram data collected from 709,000 patients over the course of 23 years, which included 2.1 million EKGs and 4 million blood potassium measurements. Two-thirds of that data were used to train a neural network to detect hyperkalemia through EKG readings.