Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students

Figure 1 The flowchart of this study. A: A cohort of 207 Chinese university students was recruited for participation in this cross-sectional study. Psychological questionnaires and resting-state heart rate variability (HRV) data were collected from each student; B: Following data preprocessing, we extracted 72 standard HRV parameters for analysis. A statistical comparison was performed between the stress and control groups; C: These HRV parameters were subsequently used as input variables for the development of six machine learning-based classification models, which were constructed using ten-fold cross-validation. The performance was evaluated, and the importance of the selected HRV parameters was assessed. ^aP < 0.05; PSS: Perceived Stress Scale; PHQ-9: Patient Health Questionnaire-9; GAD-7: Generalized Anxiety Disorder-7; ISI: Insomnia Severity Index; HRV: Heart rate variability; SHAP: SHapley Additive exPlanations; RF: Random Forest; XGBoost: EXtreme Gradient Boosting; KNN: K-Nearest Neighbors; LightGBM: Light Gradient Boosting Machine; SVC: Support Vector Machine; NB: Naive Bayes.