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.

翻译：钾离子紊乱通常无症状，但可能致命，常见于肾脏或心脏疾病患者。心电图（ECG）信号的形态对钾离子变化非常敏感，因此心电图在实验室结果出来之前检测血钾异常方面具有巨大潜力。为此，本文提出了一种基于心电图测量血钾的新系统。该系统包含三个主要步骤：首先，通过心电图与血钾测量间隔5分钟进行队列选择与数据标注，定义三类标签：低钾血症、正常和高钾血症。随后进行特征提取与选择，提取的特征包括RR间期、PR间期、QRS时限、QT间期、QTc间期、P电轴、QRS电轴、T电轴及ACCI，并采用Kruskal-Wallis技术评估特征重要性以筛选判别性特征。最后，基于选定的特征设计基于FCM聚类的ANFIS模型（FCM-ANFIS）。采用ECG-ViEW II数据库进行实验，结果表明：与其他特征相比，T电轴与血钾水平存在显著相关性（P<0.01，r=0.62）。FCM-ANFIS的绝对误差为0.4±0.3 mM，平均绝对百分比误差（MAPE）为9.99%，R方值为0.74，分类准确率为85.71%。在检测低钾血症和高钾血症时，灵敏度分别为60%和80%，特异度分别为100%和97.3%。本研究为设计无创检测血钾浓度及血钾异常的仪器提供了新思路，有助于减少心脏事件的发生。