• 1 School of Computer Science & Engineering, Taylor's University, Jalan Taylors, Subang Jaya 47500, Malaysia
  • 2 Faculty of Engineering and Technology, Sampoerna University, Jakarta 12780, Indonesia
  • 3 Department of Mechatronic and Biomedical Engineering, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Kajang 43000, Malaysia
  • 4 Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
Sensors (Basel), 2021 Sep 20;21(18).
PMID: 34577505 DOI: 10.3390/s21186300


Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.