Physiologically Explainable Ensemble Framework for Stress Classification via Respiratory Signals

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature set was constructed by extracting rhythm, depth, and nonlinear characteristics of respiratory signals. Subsequently, feature correlations and group differences across stress states were analyzed via heatmaps, multivariate analysis of variance (MANOVA), and box plots. A stacking ensemble model was then designed for three-state classification (normal/stress/meditation). Finally, Shapley additive explanations (SHAP) values were used to quantify feature contributions to classification outcomes. The leave-one-subject-out (LOSO) cross-validation results show that on the wearable stress and affect detection (WESAD) dataset, the model achieves an accuracy of 92.33% and a precision of 93.54%. Furthermore, initial validation shows key respiratory features like breath rate, inspiration time ratio, and expiratory variability coefficient align with autonomic regulation. Key respiratory metrics in other areas like rapid shallow breathing index also play an important role in the stress classification. Notably, increased respiratory depth under a stress state needs further study to clarify its physiological reasons. Overall, this framework enhances physiological interpretability while maintaining competitive performance, offering a promising approach for future applications in multimodal stress monitoring and clinical assessment.