A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers
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, ultimate...
| Authors | Albert A. Nasybullin, Nursultan Abdullaev, Maksim A. Baranov et al. |
|---|---|
| Primary category | eess.SP |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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