2025/09/27 by Maluckov, A., Stojanovic, D., Miletic, M. +2
#FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #Pattern Formation and Solitons (nlin.PS)
paper · doi:10.48550/arxiv.2509.23317
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.