Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

Ashlesha Akella, Avinash Kumar Singh, Daniel Leong, Sara Lal, Phillip Newton, Roderick Clifton-Bligh, Craig Steven McLachlan, Sylvia Maria Gustin, Shamona Maharaj, Ty Lees, Zehong Cao, Chin Teng Lin

Research output: Contribution to journalArticlepeer-review

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%.

Original languageEnglish
JournalIEEE Journal of Translational Engineering in Health and Medicine
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Australia
  • AutoEncoder
  • Electroencephalogram
  • Electroencephalography
  • Indexes
  • Physiology
  • Stress
  • Stress Classification
  • Support Vector Machine
  • Support vector machines
  • Task analysis

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