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...

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Main Authors:	Norizam, Sulaiman, Mohd Nasir, Taib, Sahrim, Lias, Zunairah, Murat, Siti Armiza, Mohd Aris, Mahfuzah, Mustafa, Nazre, Abdul Rashid
Format:	Article
Language:	English
Published:	American Scientific Publishers 2012
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/16489/ http://umpir.ump.edu.my/id/eprint/16489/ http://umpir.ump.edu.my/id/eprint/16489/ http://umpir.ump.edu.my/id/eprint/16489/1/JMIHI2012_Norizam.pdf

id	ump-16489
recordtype	eprints
spelling	ump-164892018-03-07T02:23:00Z 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) https://doi.org/10.1166/jmihi.2012.1106 DOI: 10.1166/jmihi.2012.1106
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
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
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.
format	Article
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
title	Electroencephalogram-Based Stress Index
title_short	Electroencephalogram-Based Stress Index
title_full	Electroencephalogram-Based Stress Index
title_fullStr	Electroencephalogram-Based Stress Index
title_full_unstemmed	Electroencephalogram-Based Stress Index
title_sort	electroencephalogram-based stress index
publisher	American Scientific Publishers
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/16489/ http://umpir.ump.edu.my/id/eprint/16489/ http://umpir.ump.edu.my/id/eprint/16489/ http://umpir.ump.edu.my/id/eprint/16489/1/JMIHI2012_Norizam.pdf
first_indexed	2023-09-18T22:22:13Z
last_indexed	2023-09-18T22:22:13Z
_version_	1777415732269678592

Electroencephalogram-Based Stress Index

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