Efeito dos estressores emocionais nos índices de condutância cutânea no pós‐operatório: estudo piloto de coorte prospectivo

Güngör, Semih; Storm, Hanne; Bae, James J.; Rotundo, Valeria; Christos, Paul J.

doi:10.1016/j.bjan.2020.04.009

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Eccrine glands are mainly stimulated in response to emotional events such as stress, these glands are distributed over all of the body in low densities, with a larger concentration in the face, in palms of hands, soles of feet, and armpits, with the palms of hands being the preferred location for the GSR measurement [53]. Due to the existence of electrolytes in sweat, the electric resistance decreases, and the skin conductance increases-this response is directly associated with the sympathetic nervous system (SNS) that responds during emotionally stressful stimulus [58].…”

Section: Galvanic Skin Response (Gsr)mentioning

confidence: 99%

Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

2021

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This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.

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Section: Galvanic Skin Response (Gsr)mentioning

confidence: 99%