Objective detection of chronic stress using physiological parameters

Abstract

The aim of this study was to design a system to diagnose chronic stress, based on blunted reactivity of the autonomic nervous system (ANS) to cognitive load (CL). The system concurrently measures CL-induced variations in pupil diameter (PD), heart rate (HR), pulse wave amplitude (PWA), galvanic skin response (GSR), and breathing rate (BR). Measurements were recorded from 58 volunteers whose stress level was identified using the State-Trait Anxiety Inventory. Number-multiplication questions were used as CLs. HR, PWA, GSR, and PD were significantly (p < 0.05) changed during CL. CL-induced changes in PWA (16.87 ± 21.39), GSR (- 13.71 ± 7.86), and PD (11.56 ± 9.85) for non-stressed subjects (n = 36) were significantly different (p < 0.05) from those in PWA (2.92 ± 12.89), GSR (- 6.87 ± 9.54), and PD (4.51 ± 10.94) for stressed subjects (n = 22). ROC analysis for PWA, GSR, and PD illustrated their usefulness to identify stressed subjects. By inputting all features to different classification algorithms, up to 91.7% of sensitivity and 89.7% of accuracy to identify stressed subjects were achieved using 10-fold cross-validation. This study was the first to document blunted CL-induced changes in PWA, GSR, and PD in stressed subjects, compared to those in non-stressed subjects. Preliminary results demonstrated the ability of our system to objectively detect chronic stress with good accuracy, suggesting the potential for monitoring stress to prevent dangerous stress-related diseases. Graphical abstract Chronic stress degrads the autonomic nervous system reaction to cognitive loads. Measurement of reduced changes in physiological signals during asking math questions was useful to identify people with high STAI score (stressed subjects).

Keywords: Autonomic nervous system (ANS); Physiological parameters; Pupillometry; State-Trait Anxiety Inventory (STAI); Stress.