Heart Rate Variability LSTM Autoencoder for Autonomic Fatigue Profiling in Endurance Athletes from Cundinamarca, Colombia: Code, Models and Data
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| Format: | Recurso digital |
| Langue: | espagnol |
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2026
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| author | Tellez Tinjaca, Luis Andres COLLAZOS MORALES, CARLOS ANDRES Sánchez Cifuentes, Joaquín peña, Jhonatan |
| author_facet | Tellez Tinjaca, Luis Andres COLLAZOS MORALES, CARLOS ANDRES Sánchez Cifuentes, Joaquín peña, Jhonatan |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20082945 |
| institution | Zenodo |
| language | spa |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Heart Rate Variability LSTM Autoencoder for Autonomic Fatigue Profiling in Endurance Athletes from Cundinamarca, Colombia: Code, Models and Data Tellez Tinjaca, Luis Andres COLLAZOS MORALES, CARLOS ANDRES Sánchez Cifuentes, Joaquín peña, Jhonatan Heart Rate Variability hrv LSTM Autoencoder Deep learning Autonomic Nervous System Fatigue Physical Endurance Athletes CTGAN Synthetic data Colombia <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> |
| title | Heart Rate Variability LSTM Autoencoder for Autonomic Fatigue Profiling in Endurance Athletes from Cundinamarca, Colombia: Code, Models and Data |
| topic | Heart Rate Variability hrv LSTM Autoencoder Deep learning Autonomic Nervous System Fatigue Physical Endurance Athletes CTGAN Synthetic data Colombia |
| url | https://doi.org/10.5281/zenodo.20082945 |