Psychological Stress Assessment Using Multiple Physiological Signals Based on XGBoost

被引：0
|
作者：
Lin Y. [1 ]
Long Y. [1 ]
Zhang H. [1 ]
Liu Z. [1 ]
Zhang Z. [2 ]
机构：
[1] School of Information and Electronics, Beijing Institute of Technology, Beijing
[2] General Hospital of People’s Liberation Army, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2022年 / 42卷 / 08期
关键词：
analysis of variance; classifier; physiological signals; psychological stress;
D O I：
10.15918/j.tbit1001-0645.2021.195
中图分类号：
学科分类号：
摘要：
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. © 2022 Beijing Institute of Technology. All rights reserved.
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页码：871 / 880
页数：9
相关论文
共 30 条
  • [1] WANG Weiping, XUE Zhaoxia, NIU Li, Et al., The medium function of coping style between working roleStress and mental health[J], Chinese Journal of Health Statistics, 29, 3, (2012)
  • [2] LIU Xin, ZHONG Manli, LIN Yanfei, Et al., Design and implementation of a real-time emotion recognition system based on physiological signals[J], Transactions of Beijing Institute of Technology, 39, S1, (2019)
  • [3] HERNANDEZ J, MORRIS R R, PICARD R W., Call center stress recognition with person-specific models[C], International Conference on Affective Computing and Intelligent Interaction, pp. 125-134, (2011)
  • [4] SEVIL M, HAJIZADEH I, SAMADI S, Et al., Social and competition stress detection with wristband physiological signals[C], 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 39-42, (2017)
  • [5] SUBHANI A R, MUMTAZ W, KAMIL N, Et al., MRMR based feature selection for the classification of stress using EEG[C], 2017 Eleventh International Conference on Sensing Technology (ICST), pp. 1-4, (2017)
  • [6] HAN L, ZHANG Q, CHEN X, Et al., Detecting work-related stress with a wearable device[J], Computers in Industry, 90, (2017)
  • [7] CIPRESSO P, COLOMBO D, RIVA G., Computational psychometrics using psychophysiological measures for the assessment of acute mental stress[J], Sensors, Multidisciplinary Digital Publishing Institute, 19, 4, (2019)
  • [8] ARSALAN A, MAJID M, BUTT A R, Et al., Classification of perceived mental stress using a commercially available EEG headband[J], IEEE Journal of Biomedical and Health Informatics, 23, 6, (2019)
  • [9] AHN J W, KU Y, KIM H C., A novel wearable EEG and ECG recording system for stress assessment[J], Sensors, Multidisciplinary Digital Publishing Institute, 19, 9, (2019)
  • [10] HSIEH C, CHEN Y, BEH W, Et al., Feature selection framework for XGBoost based on electrodermal activity in stress detection[C], 2019 IEEE International Workshop on Signal Processing Systems (SiPS), pp. 330-335, (2019)
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