Mental stress recognition based on electrocardiogram

In this study, we constructed a mental stress recognition model with Electrocardiogram (ECG) signals by machine learning. Firstly, we collected the ECG signals while students are explaining simple math exercises, located the R-wave peak of the QRS wave group, and calculated the R-wave to R-wave (RR) interval. Then we extracted the characteristic parameters of autonomic nervous response from the RR interval, carried out statistical analysis and sequential forward selection and finally constructed a psychological stress recognition model. The accuracy of the model in the test set and the independent subject validation set was 79% and 83%, respectively. The results show that it is feasible to recognize strong or weak psychological stress state through machine learning method.