Explainable stress detection system using hybrid CNN-Transformer and uncertainty quantification with copula GAN-based data

Stress is a widespread concern that impacts human health with its silent progression, causing significant public health burdens and economic loss globally. Non-invasive wearable technology empowered by physiological signal monitoring can enable early warning systems for stress, alleviating some of the burdens, allowing on-time interventions, and thus significantly improving quality of life. This study used the heart rate and respiratory rate data from 34 participants. It evaluated the performance of the hybrid deep learning CNN-Transformer model and benchmarked it against deep learning convolutional neural networks (CNNs) and Transformer models, extreme gradient boosting (XGBoost) and random forest (RF) machine learning models, comprising a total of five AI models. To mitigate data imbalance and observe the efficacy of deep learning data augmentation techniques in physiological signals for stress monitoring, two generative adversarial network (GAN) models: conditional tabular GAN (CTGAN), copula GAN (CopGAN) and variational autoencoder (VAE) based model tabular VAE synthesiser (TVAES) had been employed. The modelling performance significantly improved when applying CTGAN and CopGAN, demonstrating the usefulness of synthetic data. The CNN-Transformer achieved an average accuracy of 77%, a precision of 87% and an AUC of 83%. The study applied leave-one- subject- out (LOSO CV) to prove the CNN-Transformer hybrid’s robustness for generalizability to perform well for unseen subjects. The study integrated explainable AI models, Shapley values (SHAP), and local interpretable model-agnostic explanations (LIME), as well as Monte Carlo Dropout for uncertainty quantification to bring confidence, trust and transparency to AI systems, taking a step closer to real-world deployment. Similar studies can also help in the detection of other disorders, such as anxiety and depression. • Comprehensive multi-domain feature extraction from heart rate and respiratory rate using wearable non-invasive sensor technology. • Comparative evaluation of three deep learning data augmentation techniques (CTGAN, TVAES and CopulaGAN) to mitigate data imbalance. • Comparative performance analysis of the hybrid DL model, CNN-Transformer model against benchmarking DL and ML models. • Implementing uncertainty quantification using Monte Carlo Dropout for confident and reliable model prediction assessment. • Application of xAI models LIME and SHAP for interpretability and feature importance.