A Robust Transformer-based Fully Connected Neural Network Approach for Multilevel Stress Classification

• Published in: Computer and information technology pp. 3438 - 3443
• Main Authors: Rahman Khan, Md. Mushfikur, Arafat, Muhammad Abdullah
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.12.2024
• Subjects: Analytical models; Computational modeling; Correlation; Data models; Feature extraction; Human factors; machine learning; Multi-level classification; neural network; Neural networks; Predictive models; Stress; stress detection; Transformers; two-stage hybrid classifier
• ISSN: 2474-9656
• DOI: 10.1109/ICCIT64611.2024.11022102

Early identification of mental disorders, including stress, is critical as these conditions rank among the leading causes of death globally. Timely and efficient medical intervention is essential yet traditional machine-learning approaches for classifying stress levels are often complex and require further development. To confront this issue, we propose a novel two-stage hybrid machine learning approach for multi-level stress detection, utilizing an aggregate feature selection technique. Our dataset consists of raw ECG signals from ten healthy subjects collected under relaxed conditions and two stress levels, which are pre-processed by applying a combined filter and subjected to three widely recognized feature selection methods: least absolute shrinkage selection operator (LAS), information gain system (IGS), and minimum-redundancy-maximal-relevance (MRMR). The proposed model aggregates the strengths to choose the optimal feature subset. By analyzing the correlation and normality of the selected features, we propose a novel two-stage machine-learning method that leverages the capabilities of base classifiers. In the meta stage, a transformer-based classifier demonstrates a promising and robust performance, achieving a test accuracy of 94.12%. This outcome demonstrates the effectiveness of combining the advantages of base classifiers. This study will inspire more AI researchers to look into different stress detection methods.