Skin conductance responses in Major Depressive Disorder (MDD) under mental arithmetic stress

Abstract

Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states.

Fig 1. Experimental protocol.

SC signals were obtained across five phases. Each phase had a duration of 5-min.

Fig 2. Decomposition of the SC signal.

The SC signal (top black line) was decomposed into SCL (tonic component, blue) and SCR (phasic component, yellow) using the cvxEDA model. SC features were calculated from the final 60-s period of the baseline phase (P1), the first 60-s period of the MAT task (P2), and the final 60-s periods of the first recovery (P3), relaxation task (P4), and the second recovery (P5) phases, respectively.

Fig 3

Mean ± SE of (A) MSCL, (B) SDSCL, (C) SSCL, (D) MSCR, (E) NSCR, and (F) PSD. Effects of group and task on the (A)-(F) features of the MDD patients (N = 30) and the healthy controls (N = 31) were analyzed using the non-parametric equivalent of a repeated-measures ANOVA (#p < 0.05). Post-hoc comparisons of tasks were corrected using the Bonferroni method (*p < 0.05, **p < 0.01, ***p < 0.001).