ArticleIEEE journal of translational engineering in health and medicine2021
Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder.
Article in IEEE journal of translational engineering in health and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.JMIR mental health · 2024Article
- Applications of artificial intelligence-machine learning for detection of stress: a critical overview.Molecular psychiatry · 2024Review
- Comparative analysis of dimensionality reduction techniques for EEG-based emotional state classification.American journal of neurodegenerative disease · 2024Article
- ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study.PloS one · 2023Article
- Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation.IEEE journal of translational engineering in health and medicine · 2022Article
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveMental 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.
methodsTo 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.
resultsThe 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%.
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