  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.
