■高钾血症是一种潜在的危及生命的电解质紊乱,如果不及时诊断,可能导致破坏性疾病和心源性猝死。用于钾水平检查的血液采样是耗时的,并且可以延迟严重高钾血症的按时治疗。所以,我们提出了一种正确快速检测高钾血症的非侵入性方法。
■通过12导联飞利浦心电图(ECG)设备测量了转诊到ShahidRejaee医院儿科急诊室的患者的心脏信号。立刻,患者的血液样本被送到实验室进行血清钾水平测定。我们为导联2处的每个心脏信号定义了16个特征,并使用开发的算法自动提取它们。借助主成分分析(PCA)算法,进行了降维操作。决策树(DT)的算法,随机森林(RF),逻辑回归,和支持向量机(SVM)用于对血清钾水平进行分类。最后,我们使用接收器工作特性(ROC)曲线来显示结果。
■在5个月的时间内,研究包括126例血清水平高于4.5(高钾血症)的患者和152例血清钾水平低于4.5(正常钾)的患者。借助RF算法的分类具有最好的后果。准确性,Precision,回想一下,该算法的F1和曲线下面积(AUC)分别为0.71、0.87、0.53、0.66和0.69。
■基于lead2的射频分类模型可以帮助临床医生快速检测严重的运动障碍,作为一种非侵入性方法,并预防因高钾血症而危及生命的心脏病。
UNASSIGNED: Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection.
UNASSIGNED: The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results.
UNASSIGNED: In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively.
UNASSIGNED: A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.