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| Hauptverfasser: | , , , |
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| Format: | Recurso digital |
| Sprache: | Spanisch |
| Veröffentlicht: |
Zenodo
2026
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.5281/zenodo.20082945 |
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Inhaltsangabe:
- <p>This repository contains the supplementary materials for the doctoral thesis <br>"Autonomic profiling of muscular fatigue in endurance athletes from <br>Cundinamarca using Heart Rate Variability and a Deep LSTM Autoencoder" <br>(Universidad Manuela Beltrán, Bogotá, 2026).</p> <p>CONTENTS:<br>- Phase 2 pipeline: synthetic data generation (CTGAN), LSTM Autoencoder <br> training, 5-fold cross-validation, and latent space interpretability <br> analysis. Includes the trained autoencoder (.h5), encoder (.h5), and <br> fitted StandardScaler (.pkl).<br>- Phase 3 pipeline: application of the pre-trained encoder to 50 endurance <br> athletes from Cundinamarca, K-Means clustering, t-SNE/PCA visualization, <br> Mann-Whitney U tests with Bonferroni correction, and bootstrap-based <br> cluster stability validation.<br>- Jupyter notebooks (Fase II.ipynb, Fase III.ipynb), 24 result tables <br> (.xlsx), 17 publication-quality figures (300 DPI), validated synthetic <br> dataset (4,750 records × 18 HRV variables), and 8-dimensional embeddings.</p> <p>INSTRUMENTATION: Heart Rate Variability was measured using a 15-channel <br>ECG (EDAN SE-15).</p> <p>REPRODUCIBILITY: Random seed SEED=42 fixed across NumPy, TensorFlow, <br>scikit-learn, and CTGAN. Library versions pinned (sdv==1.17.0, <br>scikit-learn==1.5.2). Environment: Google Colab, Python 3.10.</p> <p>Eighteen HRV variables were analyzed: HR, RR mean/max/min, Max/Min ratio, <br>SDNN, RMSSD, NN50, pNN50, SDSD, TINN, triangular index, LF, HF, LF_norm, <br>HF_norm, LF/HF ratio, and total power.</p>