The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/d8859c20-2569-4e3a-88b3-6c12098d8c65

Ghorbani, Ramin; Reinders, Marcel; Tax, David (2024): Code underlying the publication: "Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability". Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/d8859c20-2569-4e3a-88b3-6c12098d8c65.v1

Software

• Clinical Health (organs, Diseases and Abnormal Conditions)
• Artificial Intelligence and Image Processing
• Other Information and Computing Sciences
• Health
• Information and Computing Sciences

Autoencoder, Human Activity Recognition, Inter-Subject Variability, PPG Signal, Representation Learning, Self-Supervised Learning

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Ramin Ghorbani , Marcel Reinders , David Tax

This repository provides the implementation of a Self-Supervised Learning (SSL) framework for photoplethysmography (PPG) signal representation, as detailed in the paper "Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability." The framework addresses label scarcity in PPG data analysis by utilizing signal reconstruction as a pretext task to learn informative representations, with a focus on applications such as activity recognition. The study highlights that, while SSL improves downstream supervised task performance and enables the use of simpler models, significant inter-subject variability remains a challenge, limiting the model’s generalization capabilities.

• 2024-12-20 first online, published, posted

4TU.ResearchData

Script/.py Configuration/.json Documentation/.md Dependency/.txt License/LICENSE Version Control/.gitignore, .gitattributes

Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

• https://doi.org/10.1145/3589883.3589902

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Pattern Recognition & Bioinformatics Group