Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia

Abstract

Hyperkalaemia is the medical terminology for a blood potassium level above normal parameters (greater than 5.5 mmol). This can have a variety of causes; however, patients with significant renal impairment/disease are particularly at a high risk as their kidneys are compromised and unable to filter out excess potassium from the bloodstream. Hyperkalaemia is a medical emergency and requires urgent medical intervention; if left untreated, there is an extremely high risk of cardiac arrest and death as high potassium levels directly affect the electrical activity of the heart. Cardiac activity can be observed by performing an electrocardiogram (ECG) test that is routinely used in all clinical areas worldwide. This research investigates the use of ECG tests as a diagnostic tool for the early detection of hyperkalaemia, and the role of machine learning in the prediction of blood potassium levels from ECG data alone. Support vector machines (SVMs), k-nearest neighbour (k-NN), decision tree and Gaussian Naive Bayes classifiers were used comparatively to classify ECG data as either `normokalaemia’ or ‘hyperkalaemia’. Results showed that the decision tree model performed the best, achieving a 90.9 per cent predictive accuracy. Chapter Contents: • 14.1 Introduction • 14.1.1 ECG for chronic kidney disease • 14.1.2 Non-invasive methods for potassium detection • 14.2 ECG signal analysis • 14.2.1 ECG abnormalities caused by hyperkalaemia • 14.2.2 Feature extraction and peak detection algorithm • 14.3 ECG data collection and preprocessing • 14.4 Machine learning classification models • 14.4.1 Support vector machines • 14.4.2 k-Nearest neighbour • 14.4.3 Decision tree model • 14.4.4 Gaussian Naïve Bayes model • 14.5 Results • 14.6 Conclusions and recommendations • References.