Work

Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery

Year: 2026

Type: article

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), Support Vector Machine with radial basis function kernel, k-Nearest Neighbors, Decision Tree, and Random Forest-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.

Source: Chaos An Interdisciplinary Journal of Nonlinear Science

DOI: 10.1063/5.0303657

Authors: A. Maluckov, D. B. Stojanović, M. Miletić, M. D. Ivanović, Lj. Hadžievski, J. Petrović

Institutions: University of Belgrade, Vinča Institute of Nuclear Sciences

Language: English

Topic: Heart Rate Variability and Autonomic Control

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, Science Fund of the Republic of Serbia