doi:10.1109/TAFFC.2021.3055294engGreco, A.Valenza, G.Lázaro, J.Garzón-Rey, J. M.Aguiló, J.De la Cámara, C.Bailón, RaquelScilingo, Enzo PascualeAcute stress state classification based on electrodermal activity modelingART-2023-122226Acute 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%. 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%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.2023http://zaguan.unizar.es/record/9845010.1109/TAFFC.2021.3055294http://zaguan.unizar.es/record/98450oai:zaguan.unizar.es:98450info:eu-repo/grantAgreement/ES/DGA-FSE/LMP44-18info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS groupinfo:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMONThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMONinfo:eu-repo/grantAgreement/ES/MICIU/RTI2018-097723-B-I00IEEE transactions on affective computing 14, 1 (2023), 788-799All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess