Electroencephalogram Based Stress Detection Using Extreme Learning Machine
The detection of stress is important because it contributes to diverse pathophysiological changes including sudden death. Various techniques have been used to evaluate stress in terms of questionnaire or by quantifying the changes of physiological signals. Electroencephalogram signals are highly use...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-09-01
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| Series: | Nano Biomedicine and Engineering |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.5101/nbe.v14i3.p208-215 |
| _version_ | 1797673314169126912 |
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| author | Mousa K. Wali Rashid Ali Fayadh Nabil K. Al_shamaa |
| author_facet | Mousa K. Wali Rashid Ali Fayadh Nabil K. Al_shamaa |
| author_sort | Mousa K. Wali |
| collection | DOAJ |
| description | The detection of stress is important because it contributes to diverse pathophysiological changes including sudden death. Various techniques have been used to evaluate stress in terms of questionnaire or by quantifying the changes of physiological signals. Electroencephalogram signals are highly useful in measuring human stress. Therefore, to solve and detect stress problem, this work had extracted electroencephalogram features of theta, alpha, and beta bands in the frequency domain by wavelet packet transform because these bands are concerned with stress. In this research four features have been supplied to extreme learning machine which gave accuracy of 98.56% of detecting stress from normal state based on db4 with an average sensitivity of 92.52% and specificity of 95.88%. This research studied the stress on 15 students due to mathematical exercises in a noisy environment with different stimulus. |
| first_indexed | 2024-03-11T21:43:41Z |
| format | Article |
| id | doaj.art-72d9132657964a83955ea54f5f9f9ba9 |
| institution | Directory Open Access Journal |
| issn | 2150-5578 |
| language | English |
| last_indexed | 2024-03-11T21:43:41Z |
| publishDate | 2022-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Nano Biomedicine and Engineering |
| spelling | doaj.art-72d9132657964a83955ea54f5f9f9ba92023-09-26T11:05:38ZengTsinghua University PressNano Biomedicine and Engineering2150-55782022-09-0114320821510.5101/nbe.v14i3.p208-215Electroencephalogram Based Stress Detection Using Extreme Learning MachineMousa K. Wali0Rashid Ali Fayadh1Nabil K. Al_shamaa2College of Electrical Engineering, Middle Technical University, Baghdad, IraqCollege of Electrical Engineering, Middle Technical University, Baghdad, IraqCollege of Electrical Engineering, Middle Technical University, Baghdad, IraqThe detection of stress is important because it contributes to diverse pathophysiological changes including sudden death. Various techniques have been used to evaluate stress in terms of questionnaire or by quantifying the changes of physiological signals. Electroencephalogram signals are highly useful in measuring human stress. Therefore, to solve and detect stress problem, this work had extracted electroencephalogram features of theta, alpha, and beta bands in the frequency domain by wavelet packet transform because these bands are concerned with stress. In this research four features have been supplied to extreme learning machine which gave accuracy of 98.56% of detecting stress from normal state based on db4 with an average sensitivity of 92.52% and specificity of 95.88%. This research studied the stress on 15 students due to mathematical exercises in a noisy environment with different stimulus.https://www.sciopen.com/article/10.5101/nbe.v14i3.p208-215electroencephalogramkurtosiswavelet packet transformextreme learning machine |
| spellingShingle | Mousa K. Wali Rashid Ali Fayadh Nabil K. Al_shamaa Electroencephalogram Based Stress Detection Using Extreme Learning Machine Nano Biomedicine and Engineering electroencephalogram kurtosis wavelet packet transform extreme learning machine |
| title | Electroencephalogram Based Stress Detection Using Extreme Learning Machine |
| title_full | Electroencephalogram Based Stress Detection Using Extreme Learning Machine |
| title_fullStr | Electroencephalogram Based Stress Detection Using Extreme Learning Machine |
| title_full_unstemmed | Electroencephalogram Based Stress Detection Using Extreme Learning Machine |
| title_short | Electroencephalogram Based Stress Detection Using Extreme Learning Machine |
| title_sort | electroencephalogram based stress detection using extreme learning machine |
| topic | electroencephalogram kurtosis wavelet packet transform extreme learning machine |
| url | https://www.sciopen.com/article/10.5101/nbe.v14i3.p208-215 |
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