A physiological signal-based method for early mental-stress detection. (September 2018)

• Record Type: Journal Article
• Authors: Xia, Likun; Malik, Aamir Saeed; Subhani, Ahmad Rauf
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 18
• Page End: 32
• Publication Date: 2018-09
• Subjects: Mental stress -- QEEG -- ECG -- Paired t-test -- PCA -- Support vector machine -- Kernel; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.06.004
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: The early detection of mental stress is critical for efficient clinical treatment. As compared with traditional approaches, the automatic methods presented in literature have shown significance and effectiveness in terms of diagnosis speed. Unfortunately, the majority of them mainly 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 analysing 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 paper is that the presented framework is capable of performing predictions for treatment efficacy, which is achieved by defining four stress levels and creating models for the individual level. The experimental results indicate that the framework has realised an accuracy, a sensitivity, and a specificity of 79.54%, 81%, and 78%, respectively. Moreover, the results indicate significant neurophysiological differences between the stress and control (stress-free) conditions at the individual level.