Acute Stress State Classification Based on Electrodermal Activity Modeling

• Published in: IEEE transactions on affective computing Vol. 14; no. 1; pp. 788 - 799
• Main Authors: Greco, Alberto, Valenza, Gaetano, Lazaro, Jesus, Garzon-Rey, Jorge Mario, Aguilo, Jordi, de la Camara, Concepcion, Bailon, Raquel, Scilingo, Enzo Pasquale
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Classification; Computational modeling; convex optimization; Decomposition; electrodermal activity; Feature extraction; Pattern recognition; Pattern recognition systems; Physiology; Protocols; Psychology; Robustness; Stimuli; Stress; Stress recognition; stress sources; Stress state; Support vector machines; Task analysis; trier social stress test
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2021.3055294

Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single-subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62 percent. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00 percent. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.