{"ID":2864732,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23317","arxiv_id":"2509.23317","title":"Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.23317","url_pdf":"https://arxiv.org/pdf/2509.23317v1","authors":"[\"A. Maluckov\",\"D. Stojanovic\",\"M. Miletic\",\"Lj. Hadzievski\",\"J. Petrovic\"]","published":"2025-09-27T14:04:30Z","proceeding":"nlin.PS","tasks":"[\"nlin.PS\",\"cs.LG\",\"physics.med-ph\"]","methods":"[]","has_code":false}