Physiological load estimation in athletes using ECG-derived features and gradient-boosted modeling.

Introduction: Measuring internal load is crucial for athlete training management, but many athlete monitoring tools use proprietary pipelines that lack transparency and reproducibility. Methods: This study presents a retrospective ECG/HRV-based machine learning framework for calculating session-level Training Impulse (TRIMP) using the publicly available SportDB 2.0 dataset. The reference target variable was calculated using the time spent on the session and heart-rate response, which is known as TRIMP. Supervised learning models were constructed using descriptors from the ECG, such as heart rate and heart rate variability features. TRIMP and variables directly derived from TRIMP were excluded from the predictor set to reduce circularity and improve methodological consistency. SHAP was used to evaluate the interpretability of models. Results: XGBoost showed the strongest overall cross-validation performance among the tested models. The results indicate that ECG-derived descriptors, particularly heart-rate-variability features, can reconstruct a conventional heart-rate-based internal-load reference with strong predictive performance. Discussion: The proposed framework is viewed as a clear and repeatable approach to obtain a benchmark for TRIMPs at the session level and not as a model to predict an independent physiological outcome. This study offers a free and interpretable pipeline for session-level TRIMP estimation, enabling reproducible sports analytics.