Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912611781574656 |
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| author | Maluckov, A. Stojanovic, D. Miletic, M. Hadzievski, Lj. Petrovic, J. |
| author_facet | Maluckov, A. Stojanovic, D. Miletic, M. Hadzievski, Lj. Petrovic, J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery Maluckov, A. Stojanovic, D. Miletic, M. Hadzievski, Lj. Petrovic, J. Pattern Formation and Solitons Machine Learning Medical Physics 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. |
| title | Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery |
| topic | Pattern Formation and Solitons Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2509.23317 |