Feature Extraction for Stress Detection in Electrodermal Activity

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.