用决策树筛选关键肌电特征，高效评估肌肉恢复状态。
A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers
- 用决策树分析肌电信号，自动识别重要频率和通道。
- 仅用少量关键特征即可准确分类不同休息时长。
- 适合需要透明解释的医疗与运动康复场景。
本研究提出一种基于决策树分类器的方法，用于识别肌电（EMG）数据中对评估肌肉恢复最具信息量的频率与通道。实验采集了受试者在深蹲运动中股外侧肌的肌电信号，通过不同休息间隔来评估最佳恢复时间。采用单个决策树分类器，提升模型可解释性，满足医疗与运动领域对透明性的需求。通过网格搜索调参并结合交叉验证处理类别不平衡问题，最终基于功率谱密度特征实现了可靠的休息间隔分类。结果表明，仅需少量高信息量特征即可达到足够精度，说明精简且可解释的模型在肌肉恢复评估中有效。该方法可为未来开发基于肌电的诊断模型提供参考。
原文摘要 · Abstract (English)
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.
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