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

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.