Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
A. Maluckov, D. Stojanovic, M. Miletic, Lj. Hadzievski, J. Petrovic
我们使用多式联运生物信号仪研究身体消耗后健康心脏活动的恢复动态。 来自奇点光谱的多分形特征捕获心血管调节的尺度不变性。 五个监督分类算法 - Logistic Regression(LogReg),Suport Vector Machine with RBF内核(SVM-RBF),k-Nearest Neighbors(kNN),Decict Tree(DT)和Random Forest(RF) - 进行了评估,以区分小型不平衡数据集中的恢复状态。 我们的结果表明,多分形分析与多模态传感相结合,为表征恢复提供了可靠的特征,并指出了心脏状况的非线性诊断方法。
We investigate the recovery dynamics of healthy cardiac activity after physical exertion using multimodal biosignals recorded with a polycardiograph. Multifractal features derived from the singularity spectrum capture the scale-invariant properties of cardiovascular regulation. Five supervised classification algorithms - Logistic Regression (LogReg), Suport Vector Machine with RBF kernel (SVM-RBF), k-Nearest Neighbors (kNN), Decision Tree (DT), and Random Forest (RF) - were evaluated to distingu...