A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers

TL;DR

This paper introduces a methodology using Decision Tree classifiers to identify key frequencies and channels in EMG data for muscle recovery assessment, emphasizing interpretability and feature importance.

Contribution

It presents a novel approach combining feature importance analysis with Decision Trees to streamline EMG data analysis for medical and sports applications.

Findings

A small subset of features suffices for accurate classification.

Decision Trees enhance interpretability of EMG feature importance.

The methodology effectively evaluates muscle recovery periods.

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.