Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network

This study investigates to improve the stress levels computation and its reliability using multiple physiological signals. In which, stress inducement, physiological signal acquisition, preprocessing, feature extraction, classification, optimization of features from multiple physiological signals,...

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Main Author:	Karthikeyan, Palanisamy
Format:	Thesis
Language:	English
Subjects:	Stress levels computation Stress Multiple physiological signals Fusion technique Dynamic Bayesian network (DBN)
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44123/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44123/2/full%20text.pdf
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id	my-unimap-44123
record_format	uketd_dc
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Stress levels computation Stress Multiple physiological signals Fusion technique Dynamic Bayesian network (DBN)
spellingShingle	Stress levels computation Stress Multiple physiological signals Fusion technique Dynamic Bayesian network (DBN) Karthikeyan, Palanisamy Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network
description	This study investigates to improve the stress levels computation and its reliability using multiple physiological signals. In which, stress inducement, physiological signal acquisition, preprocessing, feature extraction, classification, optimization of features from multiple physiological signals, significant feature estimation, decision boundary optimization, and fusion are the major process. Mental arithmetic task stimulus is used to induce stress on the subjects and sixty healthy subjects with a mean age of 22.5 ± 2.5 years were used. This investigation considered the five physiological signals (electrocardiogram (ECG), heart rate variability (HRV) signal, electromyogram (EMG), galvanic skin response (GSR), and skin temperature (ST)) to measure the effect of stress induced on the subject. The acquired ECG and EMG signals were preprocessed using wavelet denoising method to remove the noises in the frequency range of signals and 4th order IIR elliptic filter to remove the noises in GSR and ST signals. The ectopic beat removal algorithm was used to eliminate the presence of noise peaks and artifacts in HRV signal. In the feature extraction, ECG and EMG signals features were computed using discrete wavelet packet transform (DWPT), Lomp- Scargle (LS) periodogram is used to extract the low and high frequency band's power spectrum in the short- term HRV signal. The startle detection algorithm was implemented to extract and analyze the feature related to GSR tonic response, and finally the skin temperature features were extracted directly in the time domain. The obtained features classified in to four levels of stress including normal using three nonlinear classifiers (K nearest neighbor (KNN), probabilistic neural network (PNN), and support vector machine (SVM)). Average classification rate and F1 score above 50% and 0.5 are considered as the dominant features respectively in this work. Result indicates that, 20 features as dominant features among the 244 features investigated over various frequency bands of five physiological signals. The maximum average classification accuracy of four levels was obtained as 74.20% in mean feature of ECG, 76.69% in third cummulant feature of HRV, 74.67% in mean of EMG, 66.84% in startle frequency feature of GSR, and 63.63% in mean feature of ST in subject-independent study. The results also indicate a significant improvement of classification results in the four class of subject-independent study over the earlier highly subject-dependent studies. In order to improve the classification rate on stress levels and its reliability, the optimization of decision boundary based on physiologically significant feature vectors estimation is required. The variable-order hidden Markov model (HMM) based dynamic Bayesian network (DBN) was constructed to extract the dynamic changes of each physiological signal feature and capable to identify the significant feature vectors and decision boundaries corresponding to the different levels of stress. The DBN networks generalized the three decision boundaries of the 20 different dominant features processed, and the result shows that the maximum average Bayesian probability of each boundary is 0.544, 0.61, and 0.75 in all the states with respect to normal state. Finally, these optimized feature vectors belongs to different boundaries fused to make the global decision to ensure the reliability. The result shows that, an excellent agreement of reliability measure with improved classification accuracy while the significant components only presents in the fusion.
format	Thesis
author	Karthikeyan, Palanisamy
author_facet	Karthikeyan, Palanisamy
author_sort	Karthikeyan, Palanisamy
title	Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network
title_short	Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network
title_full	Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network
title_fullStr	Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network
title_full_unstemmed	Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network
title_sort	human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44123/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44123/2/full%20text.pdf
_version_	1747836824718409728
spelling	my-unimap-441232016-11-22T07:12:05Z Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network Karthikeyan, Palanisamy This study investigates to improve the stress levels computation and its reliability using multiple physiological signals. In which, stress inducement, physiological signal acquisition, preprocessing, feature extraction, classification, optimization of features from multiple physiological signals, significant feature estimation, decision boundary optimization, and fusion are the major process. Mental arithmetic task stimulus is used to induce stress on the subjects and sixty healthy subjects with a mean age of 22.5 ± 2.5 years were used. This investigation considered the five physiological signals (electrocardiogram (ECG), heart rate variability (HRV) signal, electromyogram (EMG), galvanic skin response (GSR), and skin temperature (ST)) to measure the effect of stress induced on the subject. The acquired ECG and EMG signals were preprocessed using wavelet denoising method to remove the noises in the frequency range of signals and 4th order IIR elliptic filter to remove the noises in GSR and ST signals. The ectopic beat removal algorithm was used to eliminate the presence of noise peaks and artifacts in HRV signal. In the feature extraction, ECG and EMG signals features were computed using discrete wavelet packet transform (DWPT), Lomp- Scargle (LS) periodogram is used to extract the low and high frequency band's power spectrum in the short- term HRV signal. The startle detection algorithm was implemented to extract and analyze the feature related to GSR tonic response, and finally the skin temperature features were extracted directly in the time domain. The obtained features classified in to four levels of stress including normal using three nonlinear classifiers (K nearest neighbor (KNN), probabilistic neural network (PNN), and support vector machine (SVM)). Average classification rate and F1 score above 50% and 0.5 are considered as the dominant features respectively in this work. Result indicates that, 20 features as dominant features among the 244 features investigated over various frequency bands of five physiological signals. The maximum average classification accuracy of four levels was obtained as 74.20% in mean feature of ECG, 76.69% in third cummulant feature of HRV, 74.67% in mean of EMG, 66.84% in startle frequency feature of GSR, and 63.63% in mean feature of ST in subject-independent study. The results also indicate a significant improvement of classification results in the four class of subject-independent study over the earlier highly subject-dependent studies. In order to improve the classification rate on stress levels and its reliability, the optimization of decision boundary based on physiologically significant feature vectors estimation is required. The variable-order hidden Markov model (HMM) based dynamic Bayesian network (DBN) was constructed to extract the dynamic changes of each physiological signal feature and capable to identify the significant feature vectors and decision boundaries corresponding to the different levels of stress. The DBN networks generalized the three decision boundaries of the 20 different dominant features processed, and the result shows that the maximum average Bayesian probability of each boundary is 0.544, 0.61, and 0.75 in all the states with respect to normal state. Finally, these optimized feature vectors belongs to different boundaries fused to make the global decision to ensure the reliability. The result shows that, an excellent agreement of reliability measure with improved classification accuracy while the significant components only presents in the fusion. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/44123 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44123/1/p.1-24.pdf 56765ff9e7be7f372bacd74ed19e22cc http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44123/2/full%20text.pdf 9d3966bb0d86e61153f6a79a4dcb3452 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44123/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Stress levels computation Stress Multiple physiological signals Fusion technique Dynamic Bayesian network (DBN) School of Mechatronic Engineering

Human stress level computation using multiple physiological signals-based on fusion technique through dynamic bayesian network

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