Noninvasive evaluation of mental stress using by a refined rough set technique based on biomedical signals

[[abstract]]Objective Evaluating and treating of stress can substantially benefits to people with health problems. Currently, mental stress evaluated using medical questionnaires. However, the accuracy of this evaluation method is questionable because of variations caused by factors such as cultural differences and individual subjectivity. Measuring of biomedical signals is an effective method for estimating mental stress that enables this problem to be overcome. However, the relationship between the levels of mental stress and biomedical signals remain poorly understood. Methods and materials A refined rough set algorithm is proposed to determine the relationship between mental stress and biomedical signals, this algorithm combines rough set theory with a hybrid Taguchi-genetic algorithm, called RS-HTGA. Two parameters were used for evaluating the performance of the proposed RS-HTGA method. A dataset obtained from a practice clinic comprising 362 cases (196 male, 166 female) was adopted to evaluate the performance of the proposed approach. Results The empirical results indicate that the proposed method can achieve acceptable accuracy in medical practice. Furthermore, the proposed method was successfully used to identify the relationship between mental stress levels and bio-medical signals. In addition, the comparison between the RS-HTGA and a support vector machine (SVM) method indicated that both methods yield good results. The total averages for sensitivity, specificity, and precision were greater than 96%, the results indicated that both algorithms produced highly accurate results, but a substantial difference in discrimination existed among people with Phase 0 stress. The SVM algorithm shows 89% and the RS-HTGA shows 96%. Therefore, the RS-HTGA is superior to the SVM algorithm. The kappa test results for both algorithms were greater than 0.936, indicating high accuracy and consistency. The area under receiver operating characteristic curve for both the RS-HTGA and a SVM method were greater than 0.77, indicating a good discrimination capability. Conclusions In this study, crucial attributes in stress evaluation were successfully recognized using biomedical signals, thereby enabling the conservation of medical resources and elucidating the mapping relationship between levels of mental stress and candidate attributes. In addition, we developed a prototype system for mental stress evaluation that can be used to provide benefits in medical practice