A Methodology to Rank Importance of Frequencies and Channels in Electromyography Data with Decision Tree Classifiers

A. A. Nasybullin ; N. Abdullaev ; M. A. Baranov ; V. V. Koshman ; V. A. Mahonin

Russian journal of nonlinear dynamics, Tome 20 (2024) no. 5, pp. 895-906

Résumé

This study presents a methodology for identifying the most informative frequencies and channels in electromyography (EMG) data to evaluate muscle recovery using Decision Tree classifiers. EMG signals, recorded from the vastus lateralis muscle during squat exercises, were analyzed across varying rest intervals to assess optimal recovery periods. By employing single Decision Tree classifiers, the study enhances interpretability, offering insights into feature importance — essential for applications in medical and sports settings where transparency is critical. The experimental protocol utilized a grid search for hyperparameter tuning and cross-validation to address class imbalance, ultimately achieving a reliable classification of rest intervals based on power spectral density features. The results indicate that a limited subset of highly informative features provides sufficient accuracy, suggesting that streamlined, interpretable models are effective for the evaluation of muscle recovery. This approach can guide future research in developing compact, robust models adapted to EMG-based diagnostics.

Détail

Keywords: electromyography (EMG), Decision Tree Classifier, tree-based models, machine learning, resting interval analysis, feature importance, ensemble methods, data preprocessing, grid search, cross-validation, interpretability, frequency analysis, biomedical signal processing, muscle recovery
Mots-clés : signal classification