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

A. Maluckov, D. Stojanovic, M. Miletic, Lj. Hadzievski, J. Petrovic
9/27/2025
nlin.PScs.LGphysics.med-ph

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Phase Space Analysis of Cardiac Spectra

83% match2023

Understanding of Normal and Abnormal Hearts by Phase Space Analysis and Convolutional Neural Networks

79% match2023

Cross-Learning Between ECG and PCG: Exploring Common and Exclusive Characteristics of Bimodal Electromechanical Cardiac Waveforms

77% match2025

Fractal and Multifractal Properties of Electrographic Recordings of Human Brain Activity: Toward Its Use as a Signal Feature for Machine Learning in Clinical Applications

77% match2 repos112018

Multifractal Recalibration of Neural Networks for Medical Imaging Segmentation

77% match2025

Training deep learning based dynamic MR image reconstruction using synthetic fractals

77% match2026

Cite this paper

@article{maluckov2025multifractal,
  title  = {Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery},
  author = {A. Maluckov and D. Stojanovic and M. Miletic and Lj. Hadzievski and J. Petrovic},
  year   = {2025},
  eprint = {2509.23317},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.23317v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2509.23317.svg)](https://codeofpaper.com/paper/2509.23317)

Discussion