NadicaSm/Statistical-Analysis-and-Machine-Learning-for-EGG-based-Nausea-Detection: v1

This is a release of the GitHub repository that presents supplementary material for the manuscript under review titled "Electrogastrogram-derived Features for Automated Sickness Detection in Driving Simulator". The repository contains both data (tables with electrogastrogram-based parameters) and R code.

Acknowledgements

Authors’ gratitude goes to Nenad B. Popović for his long collaboration in studies related to EGG research and for fruitful discussions on feature extraction techniques followed by his scientific contribution published elsewhere. The Authors also acknowledge Timotej Gruden, PhD student, for his exceptional work in experiment design and measurement conduction.

Disclaimer

The R code is provided without any guarantee and it is not intended for medical purposes.

How to cite this repository?

If you find EGG-based features and R code useful for your own research and teaching class, please cite the following references:

1. Jakus, G., Sodnik, J., & Miljković, N. (2022, October 23). NadicaSm/Statistical-Analysis-and-Machine-Learning-for-EGG-based-Nausea-Detection: v1 (Version v1). Version v1. Zenodo. https://doi.org/10.5281/zenodo.7242797
2. Jakus, G., Sodnik, J., & Miljković, N. (2022). Electrogastrogram-Derived Features for Automated Sickness Detection in Driving Simulator. Sensors, 22(22), 8616. https://doi.org/10.3390/s22228616
3. Gruden, T., Popović, N. B., Stojmenova, K., Jakus, G., Miljković, N., Tomažič, S., & Sodnik, J. (2021). Electrogastrography in autonomous vehicles—an objective method for assessment of motion sickness in simulated driving environments. Sensors, 21(2), 550. https://doi.org/10.3390/s21020550

Full Changelog: https://github.com/NadicaSm/Statistical-Analysis-and-Machine-Learning-for-EGG-based-Nausea-Detection/commits/v1