ABSTRACT
The early detection of mental stress is critical for efficient clinical treatment. Compared with traditional approaches, the automatic methods presented in literature have shown significance and effectiveness in terms of diagnosis speed. Unfortunately, the majority focus on accuracy rather than predictions for treatment efficacy. This may result in the development of methods that are less robust and accurate, which is unsuitable for clinical purposes. In this study, we propose a comprehensive framework for the early detection of mental stress by analyzing variations in both electroencephalogram (EEG) and electrocardiogram (ECG) signals from 22 male subjects (mean age: 22.54 ± 1.53 years). The significant contribution of this chapter is that the presented framework can be used to predict treatment efficacy by defining four stress levels and creating models for individual levels. The experimental results indicate that the framework has realized an accuracy, a sensitivity, and a specificity of 79.54%, 81%, and 78%, respectively. Moreover, the results indicate significant neurophysiological differences between stress and control (stress-free) conditions at the individual level.
