HEALTH: Hyperbolic Embedding and Acoustic-based Learning for Topological Hierarchies in Parkinson’s Disease
This repository contains the implementation associated with the paper:
S. Shafaati and S. H. Erfani, “HEALTH: Hyperbolic Embedding and Acoustic-based Learning for Topological Hierarchies in Parkinson’s Disease,” Proceedings of the 2025 32nd National and 10th International Iranian Conference on Biomedical Engineering (ICBME), Tabriz, Iran, pp. 37–42.
DOI: https://doi.org/10.1109/ICBME68496.2025.11392375
The codebase presents a framework for modeling Parkinson’s disease progression using a combination of hyperbolic embedding techniques and acoustic-based feature learning. The approach is designed to capture hierarchical and non-Euclidean structures inherent in biomedical data, particularly those derived from speech and acoustic signals.
📎Reference
If you use this code or find it useful in your research, please cite:
S. Shafaati and S. H. Erfani, "HEALTH: Hyperbolic Embedding and Acoustic-based Learning for Topological Hierarchies in Parkinson's Disease," 2025 32nd National and 10th International Iranian Conference on Biomedical Engineering (ICBME), Tabriz, Iran, 2025, pp. 37–42, doi: 10.1109/ICBME68496.2025.11392375.