Objective detection of chronic stress using physiological parameters

Area of Science:

• Physiology
• Psychology
• Biomedical Engineering

Background:

• Chronic stress significantly impacts the autonomic nervous system (ANS).
• Assessing ANS reactivity to cognitive load (CL) is crucial for understanding stress responses.
• Existing methods for stress diagnosis often lack objective physiological measures.

Purpose of the Study:

• To design and validate a system for diagnosing chronic stress.
• To investigate blunted ANS reactivity to CL as a biomarker for chronic stress.
• To evaluate the system's accuracy in differentiating between stressed and non-stressed individuals.

Main Methods:

• Concurrently measured CL-induced variations in pupil diameter (PD), heart rate (HR), pulse wave amplitude (PWA), galvanic skin response (GSR), and breathing rate (BR).
• Utilized number-multiplication questions as CLs for 58 volunteers.
• Assessed stress levels using the State-Trait Anxiety Inventory (STAI).

Main Results:

• Significant differences (p < 0.05) in CL-induced PWA, GSR, and PD changes were observed between stressed and non-stressed subjects.
• ROC analysis confirmed the utility of PWA, GSR, and PD in identifying stressed individuals.
• Classification algorithms achieved up to 91.7% sensitivity and 89.7% accuracy in stress detection using 10-fold cross-validation.

Conclusions:

• The study demonstrates blunted CL-induced changes in PWA, GSR, and PD in stressed individuals.
• The developed system objectively detects chronic stress with high accuracy.
• This technology holds potential for monitoring stress and preventing related diseases.