Abstract:
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.
Keywords:electromyography (EMG), signal classification, 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
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