Mental Stress Detection Using PPG Signals Based on Transformer-LSTM Model

Long-term stress not only affects physical health but also leads to the deterioration of mental state. To prevent such adverse effects, it is very important to detect and manage stress promptly. Therefore, it is particularly necessary to develop effective stress detection methods. This study utilizes the photoplethysmogram (PPG) signal in the WESAD database to develop a deep learning model for detecting psychological stress. The PPG signal is preprocessed by data filtering, down sampling, and sliding windows. The local features of the PPG signal are extracted using a convolutional neural network (CNN). Then, the Transformer model is used to analyze and capture the correlation between features. The Transformer is based on the attention mechanism, which enables the model to focus on important local features. Lastly, the long short-term memory (LSTM) is utilized to process the temporal relationship between features and detect stress status. Evaluations on binary, ternary, and quaternary stress classification tasks show significant performance: 96.07%, 92.27%, and 87.04% accuracy are achieved, respectively. Comparison with other recent research findings demonstrates the superiority of the proposed method. The proposed method does not require manual feature extraction engineering, simplifies the data processing process, and improves the accuracy of stress detection, indicating its potential in real-life and practical applications.