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

arXiv: https://arxiv.org/abs/2509.23317 · HTML: https://mlsanity.com/paper/2509.23317/

## Key facts

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- First posted on arXiv 27 Sep 2025.

## Abstract (arXiv)

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.