Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
This work addresses the need for nonlinear diagnostic methods for heart conditions, though it is incremental as it applies existing classification methods to a new dataset.
The study tackled the problem of characterizing cardiac recovery after exercise by analyzing multifractal features from multimodal biosignals, finding that these features combined with multimodal sensing reliably distinguish recovery states using supervised classification algorithms.
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.