Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machine learning technique based on an adaptive analysis of ultrasonic radiofrequency (RF) echo signals in blood is proposed, and its feasibility for classifying RBC aggregation is explored. Using an adaptive empirical wavelet transform (EWT) analysis, the ultrasonic RF signals are decomposed into a series of empirical mode functions (EMFs); then, dominant empirical mode functions (DEMFs) are selected from the series. Six statistical characteristics, including the mean, variance, median, kurtosis, root mean square (RMS), and skewness are calculated for the locally normalized DEMFs, aiming to form primary feature vectors. Random forest (RDF) and support vector machine (SVM) classifiers are trained with the given feature vectors to obtain prediction models for RBC classification. Ultrasonic RF echo signals are acquired from five groups of six types of porcine blood samples with average numbers of aggregated RBCs of 1.04, 1.20, 1.83, 2.31, 2.72, and 4.28, respectively, to test the classification performance of the proposed method. The best subset with regard to the variance, kurtosis, and RMS is determined according to the maximum accuracy based on the RDF and SVM classifiers. The classification accuracies are 84.03 ± 3.13% for the RDF classifier, and 85.88 ± 2.99% for the SVM classifier. The mean classification accuracy of the SVM classifier is 1.85% better than that of the RDF classifier. In conclusion, the machine learning method is useful for the discrimination of varying degrees of RBC aggregation, and has potential for use in characterizing and monitoring the RBC aggregation in vessels.

分级红细胞（RBC）聚集对于早期诊断和预防相关疾病（例如缺血性心脑血管疾病，II型糖尿病，深静脉血栓形成和镰状细胞病）非常重要。在这项研究中，提出了一种基于自适应分析血液中超声射频回波信号的机器学习技术，并探讨了其对RBC聚集进行分类的可行性。使用自适应经验小波变换（EWT）分析，将超声RF信号分解为一系列经验模式函数（EMF）。然后，从序列中选择主导经验模式函数（DEMF）。为本地标准化的DEMF计算了六个统计特征，包括均值，方差，中位数，峰度，均方根（RMS）和偏度，旨在形成主要特征向量。使用给定的特征向量训练随机森林（RDF）和支持向量机（SVM）分类器，以获得用于RBC分类的预测模型。从六种类型的猪血样的五组中获取超声RF回波信号，其平均总RBC数分别为1.04、1.20、1.83、2.31、2.72和4.28，以测试该方法的分类性能。关于方差，峰度和RMS的最佳子集是根据基于RDF和SVM分类器的最大精度确定的。RDF分类器的分类精度为84.03±3.13％，SVM分类器的分类精度为85.88±2.99％。SVM分类器的平均分类准确度比RDF分类器的平均分类准确度高1.85％。综上所述，