Automated stress detection with explainable-AI transformer TabNet technique and physiological signals

S. Janifer Jabin Jui; Ravinesh C. Deo; Rajendra Acharya; Prabal Datta Barua; Jeffrey Soar; Aruna Devi

doi:10.1016/j.bspc.2025.109208

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Automated stress detection with explainable-AI transformer TabNet technique and physiological signals

Journal article

Open access

Peer reviewed

Automated stress detection with explainable-AI transformer TabNet technique and physiological signals

S. Janifer Jabin Jui, Ravinesh C. Deo, Rajendra Acharya, Prabal Datta Barua, Jeffrey Soar and Aruna Devi

Biomedical Signal Processing and Control, Vol.113, pp.1-20

2026

DOI: https://doi.org/10.1016/j.bspc.2025.109208

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Abstract

Data augmentation

Explainable AI

Interpretable model

Stress detection

TabNet classifier machine learning

Psychological stress has a significant impact on human well-being, behaviour and health that incurs significant economic costs. This study is designed to develop predictive models that can potentially assist in the diagnosis and management of psychological stress through artificial intelligence (AI). We propose an explainable-AI deep learning Transformer-based Attentive Interpretable Tabular Learning (TabNet) model that captures multimodal imbalanced data comprised of the derivative of heart rates (HR) and electrodermal activities (EDA) of 99 subjects from four publicly available datasets (i.e., WESAD, SWELL, NEURO and UBFC-Phys). The proposed model creates the generalizability of the representative dataset, with transfer learning used as a key to identifying the stress with HR and EDA variables. The proposed stress classification model performed relatively well in detecting the stress with 76% accuracy, 77% precision, 76% recall and a F1 score of 74% and the leave one subject out (LOSO) cross-validation strategy. The proposed model also considered the variability of subject-wise stress with the capability to automate the feature selection process using the attention mechanisms. Finally, the model has been tested using a synthetic dataset with a Synthetic Minority Over-sampling Technique (SMOTE) for the imbalanced dataset and the study has used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) for model explainability that can bring greater trustworthiness of the proposed model for its future exploration in clinical healthcare environments.

Details

Title: Automated stress detection with explainable-AI transformer TabNet technique and physiological signals
Authors: S. Janifer Jabin Jui (Corresponding Author) - University of Southern Queensland
Ravinesh C. Deo (Corresponding Author) - University of Southern Queensland
Rajendra Acharya - University of Southern Queensland
Prabal Datta Barua - University of Southern Queensland
Jeffrey Soar - University of Southern Queensland
Aruna Devi - University of the Sunshine Coast, Queensland, School of Education and Tertiary Access
Publication details: Biomedical Signal Processing and Control, Vol.113, pp.1-20
Publisher: Elsevier BV
Date published: 2026
DOI: 10.1016/j.bspc.2025.109208
ISSN: 1746-8108; 1746-8094
Data Availability: The authors are unable or have chosen not to specify which data has been used.
Grant note: This work was supported by the University of Southern Queensland International PhD Fee Scholarship (2022–2025) and an International PhD Stipend Scholarship from Cogninet Australia Pty Ltd. , under the Industry Research Scholarship Agreement.
Organisation Unit: School of Education and Tertiary Access
Language: English
Record Identifier: 991184167202621
Output Type: Journal article

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