基于 XGBoost 的多种生理信号评估心理压力等级方法

Objective assessment of psychological stress using physiological signals has become a current research hotspot, but the best algorithm needs to be further explored. In this study, a mental arithmetic task was conducted to induce psychological stress in subjects. Four physiological signals including EEG, ECG, skin conductance, and pulse wave were collected from 21 university students. The features of the time and frequency domains for physiological signals were extracted. Six methods including ANOVA, mRMR, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) were utilized to select effective features. SVM, K-Nearest Neighbor (KNN), Gaussian Naive Bayesian (GNB), Adaptive Boosting (Adaboost), GBDT, and XGBoost were conducted to classify the extracted features. The results show that the combined model of GBDT feature selection and XGBoost classifier is the most effective for the assessment of psychological stress on different levels.