A Novel Framework for Forecasting Mental Stress Levels Based on Physiological Signals

Mental stress may negatively affect individual health, life and work. Early intervention of such disease may help improve the quality of their lives and avoid major accidents caused by stress. Unfortunately, detection of stress levels at the present time can hardly meet the requirements such as stress regulations, which is due to the lack of anticipation of future stress changes, thus it is necessary to forecast the state of mental stress. In this study, we propose a supervised learning framework to forecast mental stress levels. Firstly, we extract a series of features of physiological signals including electroencephalography (EEG) and electrocardiography (ECG); secondly, we apply various autoregressive (AR) models to forecast stress features based on the extracted features; finally, the forecasted features are fed into several conventional machine learning based classification models to achieve forecasting of mental stress levels at subsequent time steps. We compare the effectiveness of the proposed framework on three competitive methods using three different datasets. The experimental results demonstrate that our proposed method outperforms those three methods and achieve a better forecasting accuracy of 89.65%. In addition, we present a positive correlation between mental state changes and forecasting result on theta spectrum at the frontal region.