通过多模态心电信号分析运动后心率恢复的非线性特征。
Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
- 基于奇异谱的多分形分析捕捉心血管调节的尺度不变性。
- 五种分类算法在小样本不平衡数据上有效区分恢复状态。
- 为心脏病非线性诊断提供新思路，适合心律研究者参考。
我们利用多导心电图仪记录的多模态生物信号，研究健康个体运动后心脏活动的恢复动力学。通过奇异谱提取的多分形特征，能够捕捉心血管调控的尺度不变性。在小规模且不平衡的数据集上，评估了五种监督分类算法：逻辑回归（LogReg）、径向基函数核支持向量机（SVM-RBF）、k近邻（kNN）、决策树（DT）和随机森林（RF），用于区分恢复状态。结果表明，结合多分形分析与多模态传感可生成可靠特征，有助于揭示心律失常等心脏疾病的非线性诊断潜力。
原文摘要 · Abstract (English)
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 distinguish recovery states in a small, imbalanced dataset. Our results show that multifractal analysis, combined with multimodal sensing, yields reliable features for characterizing recovery and points toward nonlinear diagnostic methods for heart conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。