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EEG Based Classification of Long-Term Stress Using Psychological Labeling
Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180785/ https://www.ncbi.nlm.nih.gov/pubmed/32235295 http://dx.doi.org/10.3390/s20071886 |
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author | Saeed, Sanay Muhammad Umar Anwar, Syed Muhammad Khalid, Humaira Majid, Muhammad Bagci, Ulas |
author_facet | Saeed, Sanay Muhammad Umar Anwar, Syed Muhammad Khalid, Humaira Majid, Muhammad Bagci, Ulas |
author_sort | Saeed, Sanay Muhammad Umar |
collection | PubMed |
description | Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation. |
format | Online Article Text |
id | pubmed-7180785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71807852020-05-01 EEG Based Classification of Long-Term Stress Using Psychological Labeling Saeed, Sanay Muhammad Umar Anwar, Syed Muhammad Khalid, Humaira Majid, Muhammad Bagci, Ulas Sensors (Basel) Article Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation. MDPI 2020-03-29 /pmc/articles/PMC7180785/ /pubmed/32235295 http://dx.doi.org/10.3390/s20071886 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Saeed, Sanay Muhammad Umar Anwar, Syed Muhammad Khalid, Humaira Majid, Muhammad Bagci, Ulas EEG Based Classification of Long-Term Stress Using Psychological Labeling |
title | EEG Based Classification of Long-Term Stress Using Psychological Labeling |
title_full | EEG Based Classification of Long-Term Stress Using Psychological Labeling |
title_fullStr | EEG Based Classification of Long-Term Stress Using Psychological Labeling |
title_full_unstemmed | EEG Based Classification of Long-Term Stress Using Psychological Labeling |
title_short | EEG Based Classification of Long-Term Stress Using Psychological Labeling |
title_sort | eeg based classification of long-term stress using psychological labeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180785/ https://www.ncbi.nlm.nih.gov/pubmed/32235295 http://dx.doi.org/10.3390/s20071886 |
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