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

A. Maluckov; D. Stojanovic; M. Miletic; Lj. Hadzievski; J. Petrovic

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arXiv · 2509.23317

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

A. Maluckov·D. Stojanovic·M. Miletic·Lj. Hadzievski·J. Petrovic

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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.

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A. Maluckov, D. Stojanovic, M. Miletic, Lj. Hadzievski, J. Petrovic. 2025-09-27. Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery. https://arxiv.org/abs/2509.23317

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