Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder

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. Electroence...

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Bibliographic Details
Published in:IEEE journal of translational engineering in health and medicine 2021-01, Vol.9, p.1-9
Main Authors: Akella, Ashlesha, Singh, Avinash Kumar, Leong, Daniel, Lal, Sara, Newton, Phillip, Clifton-Bligh, Roderick, Mclachlan, Craig Steven, Gustin, Sylvia Maria, Maharaj, Shamona, Lees, Ty, Cao, Zehong, Lin, Chin-Teng
Format: Article
Language:English
Subjects:
Australia
Autoencoder
Biomarkers
Brain
Coders
electroencephalogram
Electroencephalography
Frequencies
Indexes
Machine learning
Physiology
Psychological stress
Representations
Stress
stress classification
support vector machine
Support vector machines
Task analysis
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Summary: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%.
ISSN:2168-2372
2168-2372
DOI:10.1109/JTEHM.2021.3077760