A data-driven approach using Limited Penetrable Visibility Graphs on simultaneous EEG and ECG signals demonstrated feasibility and effectiveness for differentiating stress from physiological baseline.
A novel data-driven approach using LPVG and machine learning on simultaneous EEG and ECG signals can effectively detect cognitive stress during physical activity.
Stress is a psychological concept defined as an ensemble of coping responses to a perceived threat. It has received tremendous, well-placed scientific attention given its impact on individual health and performance. While acute exposure is associated with several physiological and psychological diseases; stress proved beneficial in some circumstances. It is therefore relevant to investigate it experimentally by inducing stress in participants through validated stressors that can elicit different forms of stress. To counter the personal bias embedded in questionnaires for stress assessment, physiological signals give access to a more objective, personalized response. Stress generates a wide range of reactions in terms of neurophysiological activity assessed using electroencephalography (EEG). Cardiac activity also proved relevant to analyze changes in heart rate and rhythm in electrocardiography (ECG) signals. In this paper, we propose a data-driven approach for differentiating stress from physiological baseline based on the multi-modal PASS database. Data is from mobile, simultaneous recording of EEG and ECG data, in different settings combination of stress elicitation and physical activity intensity. The method leverages the use of Limited Penetrable Visibility Graphs (LPVG) by extracting various features from images of adjacency matrices of the signals; including frequency-based and shape-based features. These features were then input into different machine-learning models. The proposed approach's performance was rigorously evaluated using real data. The obtained results provide compelling evidence supporting the feasibility and effectiveness of the proposed method for stress detection.
Sadoun et al. (Mon,) conducted a other in Cognitive stress. Limited Penetrable Visibility Graphs (LPVG) and machine learning vs. Physiological baseline was evaluated on Differentiating stress from physiological baseline. A data-driven approach using Limited Penetrable Visibility Graphs on simultaneous EEG and ECG signals demonstrated feasibility and effectiveness for differentiating stress from physiological baseline.