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Classifying multi-level stress responses from brain cortical EEG in nurses and non-health professionals using machine learning auto encoder

Citation

Akella, A and Singh, AK and Leong, D and Lal, S and Newton, P and Clifton-Bligh, R and Mclachlan, CS and Gustin, SM and Maharaj, S and Lees, T and Cao, Z and Lin, C-T, Classifying multi-level stress responses from brain cortical EEG in nurses and non-health professionals using machine learning auto encoder, IEEE Journal of Translational Engineering in Health and Medicine, 9 Article 2200109. ISSN 2168-2372 (2021) [Refereed Article]


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Copyright 2021 the authors. This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. For more information, see https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1109/JTEHM.2021.3077760

Abstract

Objective: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.

Methods: To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.

Results: The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.

Item Details

Item Type:Refereed Article
Keywords:autoencoder, support vector machine, stress classification, electroencephalogram
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:144667
Year Published:2021
Deposited By:Information and Communication Technology
Deposited On:2021-06-03
Last Modified:2021-09-06
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