Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity.
Int J Neural Syst
; 30(7): 2050031, 2020 Jul.
Article
en En
| MEDLINE
| ID: mdl-32507059
ABSTRACT
Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate identification of stress/calm condition from electrodermal activity (EDA) signals. The acquisition of EDA signals from a commercial wearable as well as their storage and processing are presented. Several time-domain, frequency-domain and morphological features are extracted over the skin conductance response of the EDA signals. Afterwards, a classification is undergone by using several classical support vector machines (SVMs) and deep support vector machines (D-SVMs). In addition, several binary classifiers are also compared with SVMs in the stress/calm identification task. Moreover, a series of video clips evoking calm and stress conditions have been viewed by 147 volunteers in order to validate the classification results. The highest F1-score obtained for SVMs and D-SVMs are 83% and 92%, respectively. These results demonstrate that not only classical SVMs are appropriate for classification of biomarker signals, but D-SVMs are very competitive in comparison to other classification techniques. In addition, the results have enabled drawing useful considerations for the future use of SVMs and D-SVMs in the specific case of stress/calm identification.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Estrés Psicológico
/
Electrodiagnóstico
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Máquina de Vectores de Soporte
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Aprendizaje Profundo
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Respuesta Galvánica de la Piel
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
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Screening_studies
Límite:
Adult
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Humans
Idioma:
En
Año:
2020
Tipo del documento:
Article