ABSTRACT

The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal dynamics. Based on these characteristics, we extracted multimodal features from congestive heart failure (CHF) and normal sinus rhythm (NSR) signals. We performed the synthetic minority over-sampling technique (SMOTE) to increase the number of CHF subjects to balance our train data. The classification between these subjects with original data and SMOTE data was performed using machine learning classifiers such as classification and regression tree (CART), support vector machine linear (SVM-L), Naïve Bayes, neural network, and ensemble classifiers such as random forest (RF), XG boost, averaged neural network (AVNNET). With the original data, the highest performance was obtained using SVM-L with accuracy (94.28%), sensitivity (84.61%), specificity (100%), p-value (0.0002), AUC (0.9605) with 95% CI: 0.9006-1.00. By applying the SMOET, the highest performance was obtained with SVM-L with accuracy (97.14%), sensitivity (92.30%), specificity (100%), p-value (7.99e-06), AUC (0.9650) with 95% CI: 0.8945–1.00. The results reveal that proposed approach with SMOTE improved the detection performance which can be very effective and computationally efficient tool for automatic detection of congestive heart failure patients.

摘要

对于 65 岁以上的美国人，充血性心力衰竭 (CHF) 的发病率约为千分之 10。CHF 的动力学是高度复杂的、非线性的和时间动力学的。基于这些特征，我们从充血性心力衰竭 (CHF) 和正常窦性心律 (NSR) 信号中提取了多模态特征。我们执行了合成少数过采样技术 (SMOTE) 以增加 CHF 受试者的数量以平衡我们的训练数据。使用机器学习分类器（如分类和回归树 (CART)、支持向量机线性 (SVM-L)、朴素贝叶斯、神经网络和集成分类器（如随机森林））对这些主题与原始数据和 SMOTE 数据进行分类(RF)、XG 增强、平均神经网络 (AVNNET)。有了原始数据，p值 (0.0002)，AUC (0.9605)，95% CI：0.9006-1.00。通过应用 SMOET，SVM-L 获得了最高性能，准确度 (97.14%)、灵敏度 (92.30%)、特异性 (100%)、p值 (7.99e-06)、AUC (0.9650) 为 95% CI：0.8945–1.00。结果表明，所提出的 SMOTE 方法提高了检测性能，这对于自动检测充血性心力衰竭患者来说是一种非常有效且计算效率高的工具。