Acute stress state classification based on electrodermal activity modeling

Alberto Greco, Gaetano Valenza, Jesus Lazaro, Jorge Mario Garzon-Rey, Jordi Aguilo, Concepcion De-la-Camara, Raquel Bailon, Enzo Pasquale Scilingo

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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%. 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.

Original languageEnglish
JournalIEEE Transactions on Affective Computing
DOIs
Publication statusAccepted in press - 2021

Keywords

  • Computational modeling
  • Convex optimization
  • Electrodermal Activity
  • Feature extraction
  • Physiology
  • Protocols
  • Psychology
  • Stress
  • Stress recognition
  • Stress sources
  • Task analysis
  • Trier Social Stress Test

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