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Kohonen Neural Network Stress Detection Using Only Electrodermal Activity FeaturesBORNOIU, I.-V. , GRIGORE, O.
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biomedical signal processing, data analysis, electrophysiology, pattern recognition, self organizing feature maps
stress(7), electrodermal(6), activity(5), emotion(4)
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About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 71 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03009
Web of Science Accession Number: 000340869800009
SCOPUS ID: 84907310113
This paper presents a method for identifying human stress levels by using a Kohonen neural network. The study focuses on differentiating between a relaxed and a stressed state and it presents a series of parameters (skin conductance response signal power, skin conductance response signal frequency, skin conductance level gradient, response rise time and response amplitude) extracted only from the electrodermal activity signal. A very strict recording protocol was used to minimize the artifacts caused by the bad connection between electrodes and skin. A stress inducing method is presented that can be used to replicate results in laboratory conditions.
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 Social and competition stress detection with wristband physiological signals, Sevil, Mert, Hajizadeh, Iman, Samadi, Sediqeh, Feng, Jianyuan, Lazaro, Caterina, Frantz, Nicole, Yu, Xia, Brandt, Rachel, Maloney, Zacharie, Cinar, Ali, 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), ISBN 978-1-5090-6244-7, 2017.
Digital Object Identifier: 10.1109/BSN.2017.7936002 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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