Human Stress Classification Using Cardiovascular and Respiratory Data Based on Machine Learning Techniques

Abstract Background: Stress, a widespread mental health concern, significantly impacts people well-being and performance. This study proposes a novel approach to stress detection by fusing cardiovascular and respiratory data. Methods: Fifteen participants underwent a mental stress induction task while their electrocardiogram (ECG) and respiration signals were recorded. A real-time peak detection algorithm was developed for ECG signal processing, and both time and frequency domain features were extracted from ECG and respiration signals. Various machine learning models, including Support Vector Machine, K-Nearest Neighbors, bagged decision trees, and random forests, were employed for classification, with accurate labeling achieved through the NASA-TLX questionnaire. Results: The results demonstrate that combining respiration and cardiovascular features significantly enhances stress classification performance compared to using each modality alone, achieving an accuracy of 95.6% ±1.7%. Forward feature selection identifies key discriminative features from both modalities. Conclusions: This study demonstrates the efficacy of multimodal physiological data integration for accurate stress detection, outperforming single-modality approaches and comparable studies in the literature. The findings highlight the potential of real-time monitoring systems in enhancing stress and health management.