Determination and classification of human stress index using non-parametric analysis of EEG signals

Regardless of type of stress, either mental stress, emotional stress or physical stress, it definitely affects human lifestyle and work performance. There are two prominent methods in assessing stress which are psychological assessment (qualitative method) and physiological assessment (quantitative...

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Main Author:	Norizam, Sulaiman
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/16490/1/Determination%20and%20classification%20of%20human%20stress%20index%20using%20nonparametric%20analysis%20of%20EEG%20signals.pdf
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spelling	my-ump-ir.164902023-06-14T08:18:55Z Determination and classification of human stress index using non-parametric analysis of EEG signals 2015-11-03 Norizam, Sulaiman TK Electrical engineering. Electronics Nuclear engineering Regardless of type of stress, either mental stress, emotional stress or physical stress, it definitely affects human lifestyle and work performance. There are two prominent methods in assessing stress which are psychological assessment (qualitative method) and physiological assessment (quantitative method). This research proposes a new stress index based on Electroencephalogram (EEG) signals and non-parametric analysis of the signals. In non-parametric method, the EEG features that might relate to stress are extracted in term of Asymmetry Ratio (AR), Relative Energy Ratio (RER), Spectral Centroids (SC) and Spectral Entropy (SE). The selected features are fed to the k-Nearest Neighbor (k-NN) classifier to identify the stressed group among the four experimental groups being tested. The classification results are based on accuracy, sensitivity and specificity. To support the classification results using k-NN classifier, the clustering techniques using Fuzzy C-Means (FCM) and Fuzzy K-Means (FKM) are implemented. To ensure the robustness of the classifier, the crossvalidation technique using k-fold and leave-one-out is performed to the classifier. The assignment of the stress index is verified by applying Z-score technique to the selected EEG features. The experiments established a 3-level index (Index 1, Index 2 and Index 3) which represents the stress levels of low stress, moderate stress and high stress at overall classification accuracy of 88.89%, classification sensitivity of 86.67 % and classification specificity of 100%. The outcome of the research suggests that the stress level of human can be determined accurately by applying SC on the ratio of the Energy Spectral Density (ESD) of Beta and Alpha bands of the brain signals. The experimental results of this study also confirm that human stress level can be determined and classified precisely using physiological signal through the proposed stress index. The high accuracy, sensitivity and specificity of the classifier might also indicate the robustness of the proposed method. 2015-11 Thesis http://umpir.ump.edu.my/id/eprint/16490/ http://umpir.ump.edu.my/id/eprint/16490/1/Determination%20and%20classification%20of%20human%20stress%20index%20using%20nonparametric%20analysis%20of%20EEG%20signals.pdf pdf en public phd doctoral Universiti Teknologi MARA Faculty of Electrical Engineering Taib, Mohd Nasir
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Taib, Mohd Nasir
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Norizam, Sulaiman Determination and classification of human stress index using non-parametric analysis of EEG signals
description	Regardless of type of stress, either mental stress, emotional stress or physical stress, it definitely affects human lifestyle and work performance. There are two prominent methods in assessing stress which are psychological assessment (qualitative method) and physiological assessment (quantitative method). This research proposes a new stress index based on Electroencephalogram (EEG) signals and non-parametric analysis of the signals. In non-parametric method, the EEG features that might relate to stress are extracted in term of Asymmetry Ratio (AR), Relative Energy Ratio (RER), Spectral Centroids (SC) and Spectral Entropy (SE). The selected features are fed to the k-Nearest Neighbor (k-NN) classifier to identify the stressed group among the four experimental groups being tested. The classification results are based on accuracy, sensitivity and specificity. To support the classification results using k-NN classifier, the clustering techniques using Fuzzy C-Means (FCM) and Fuzzy K-Means (FKM) are implemented. To ensure the robustness of the classifier, the crossvalidation technique using k-fold and leave-one-out is performed to the classifier. The assignment of the stress index is verified by applying Z-score technique to the selected EEG features. The experiments established a 3-level index (Index 1, Index 2 and Index 3) which represents the stress levels of low stress, moderate stress and high stress at overall classification accuracy of 88.89%, classification sensitivity of 86.67 % and classification specificity of 100%. The outcome of the research suggests that the stress level of human can be determined accurately by applying SC on the ratio of the Energy Spectral Density (ESD) of Beta and Alpha bands of the brain signals. The experimental results of this study also confirm that human stress level can be determined and classified precisely using physiological signal through the proposed stress index. The high accuracy, sensitivity and specificity of the classifier might also indicate the robustness of the proposed method.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Norizam, Sulaiman
author_facet	Norizam, Sulaiman
author_sort	Norizam, Sulaiman
title	Determination and classification of human stress index using non-parametric analysis of EEG signals
title_short	Determination and classification of human stress index using non-parametric analysis of EEG signals
title_full	Determination and classification of human stress index using non-parametric analysis of EEG signals
title_fullStr	Determination and classification of human stress index using non-parametric analysis of EEG signals
title_full_unstemmed	Determination and classification of human stress index using non-parametric analysis of EEG signals
title_sort	determination and classification of human stress index using non-parametric analysis of eeg signals
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/16490/1/Determination%20and%20classification%20of%20human%20stress%20index%20using%20nonparametric%20analysis%20of%20EEG%20signals.pdf
_version_	1783732012019875840

Determination and classification of human stress index using non-parametric analysis of EEG signals

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