Electroencephalogram-Based Stress Index
Stress is one of the major health issues where too much stress may lead to depression, fatigue and insomnia. Various methods have been introduced by researchers to detect and analyze stress level using human physiological signals but yet to come out with a reliable indicator which able to indicate t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Scientific Publishers
2012
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/16489/1/JMIHI2012_Norizam.pdf |
| _version_ | 1825823524446535680 |
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| author | Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid |
| author_facet | Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid |
| author_sort | Norizam, Sulaiman |
| collection | UMP |
| description | Stress is one of the major health issues where too much stress may lead to depression, fatigue and insomnia. Various methods have been introduced by researchers to detect and analyze stress level using human physiological signals but yet to come out with a reliable indicator which able to indicate the stress level of healthy human from their brain electrical activity; Electroencephalogram (EEG) signals. This study proposes stress index as an indicator of stress level using EEG signals. The study employs nonparametric method to extract stress features from EEG signals after performing two tasks; do nothing and answer Intelligence Quotient (IQ) test questions. The k-Nearest Neighbor (k-NN) classifier is used to identify the stressed group using the extracted stress features. The results of the study established 3 type of indexes which represent the stress levels (Low Stress, Moderate Stress, High Stress) with 88.89% overall classification accuracy, 86.67% classification sensitivity and 100% classification specificity. The 10-fold and leave-one-out cross validation of the classifier produced classification accuracy of 78.89% and 83.50% respectively. |
| first_indexed | 2024-03-06T12:12:30Z |
| format | Article |
| id | UMPir16489 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:12:30Z |
| publishDate | 2012 |
| publisher | American Scientific Publishers |
| record_format | dspace |
| spelling | UMPir164892018-03-07T02:23:00Z http://umpir.ump.edu.my/id/eprint/16489/ Electroencephalogram-Based Stress Index Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid TK Electrical engineering. Electronics Nuclear engineering Stress is one of the major health issues where too much stress may lead to depression, fatigue and insomnia. Various methods have been introduced by researchers to detect and analyze stress level using human physiological signals but yet to come out with a reliable indicator which able to indicate the stress level of healthy human from their brain electrical activity; Electroencephalogram (EEG) signals. This study proposes stress index as an indicator of stress level using EEG signals. The study employs nonparametric method to extract stress features from EEG signals after performing two tasks; do nothing and answer Intelligence Quotient (IQ) test questions. The k-Nearest Neighbor (k-NN) classifier is used to identify the stressed group using the extracted stress features. The results of the study established 3 type of indexes which represent the stress levels (Low Stress, Moderate Stress, High Stress) with 88.89% overall classification accuracy, 86.67% classification sensitivity and 100% classification specificity. The 10-fold and leave-one-out cross validation of the classifier produced classification accuracy of 78.89% and 83.50% respectively. American Scientific Publishers 2012-07-06 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16489/1/JMIHI2012_Norizam.pdf Norizam, Sulaiman and Mohd Nasir, Taib and Sahrim, Lias and Zunairah, Murat and Siti Armiza, Mohd Aris and Mahfuzah, Mustafa and Nazre, Abdul Rashid (2012) Electroencephalogram-Based Stress Index. Journal of Medical Imaging and Health Informatics, 2 (3). pp. 327-335. ISSN 2156-7018 (Print); 2156-7026 (Online). (Published) https://doi.org/10.1166/jmihi.2012.1106 DOI: 10.1166/jmihi.2012.1106 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Mahfuzah, Mustafa Nazre, Abdul Rashid Electroencephalogram-Based Stress Index |
| title | Electroencephalogram-Based Stress Index |
| title_full | Electroencephalogram-Based Stress Index |
| title_fullStr | Electroencephalogram-Based Stress Index |
| title_full_unstemmed | Electroencephalogram-Based Stress Index |
| title_short | Electroencephalogram-Based Stress Index |
| title_sort | electroencephalogram based stress index |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/16489/1/JMIHI2012_Norizam.pdf |
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