Feature Extraction for Stress Detection in Electrodermal Activity

Erika Lutin, Erika Lutin, Ryuga Hashimoto, Walter De Raedt, Chris Van Hoof, Chris Van Hoof, Chris Van Hoof

2021

Abstract

Electrodermal activity (EDA) is a sensitive measure for changes in the sympathetic system, reflecting emotional and cognitive states such as stress. There is, however, inconsistency in the recommendations on which features to extract. In this study, we brought together different feature extraction methods: trough-to-peak features, decomposition-based features, frequency features and time-frequency features. Regarding the decomposition analysis, three different applications were used: Ledalab, cvxEDA and sparsEDA. A total of forty-seven features was extracted from a previously collected dataset. This dataset included twenty participants performing three different stress tasks. A Support Vector Machine (SVM) classifier was built in a Leave-One-Subject-Out Cross Validation (LOOCV) set-up with feature selection within the LOOCV loop. Three features were consistently selected over all participants: 1) the number of responses in the driver function generated by Ledalab and 2) by sparsEDA and 3) a time-frequency feature, previously described as TVSymp. The classifier obtained an accuracy of 88.52%, a sensitivity of 72.50% and a specificity of 93.65%. This research shows that EDA can be successfully used in stress detection, without the addition of any other physiological signals. The classifier, built with the most recent feature extraction methods in literature, was found to outperform previous classification attempts.

Paper Citation

in Harvard Style

Lutin E., Hashimoto R., De Raedt W. and Van Hoof C. (2021). Feature Extraction for Stress Detection in Electrodermal Activity. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS; ISBN 978-989-758-490-9, SciTePress, pages 177-185. DOI: 10.5220/0010244600002865

in Bibtex Style

@conference{biosignals21,
author={Erika Lutin and Ryuga Hashimoto and Walter De Raedt and Chris Van Hoof},
title={Feature Extraction for Stress Detection in Electrodermal Activity},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS},
year={2021},
pages={177-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010244600002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS
TI - Feature Extraction for Stress Detection in Electrodermal Activity
SN - 978-989-758-490-9
AU - Lutin E.
AU - Hashimoto R.
AU - De Raedt W.
AU - Van Hoof C.
PY - 2021
SP - 177
EP - 185
DO - 10.5220/0010244600002865
PB - SciTePress