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.

Bispectral Index Alterations and Associations With Autonomic Changes During Hypnosis in Trauma Center Researchers: Formative Evaluation Study

“…On the other hand, biofeedback relaxation has been found to be associated with increases in peripheral skin temperature [41]. Electrical skin conductance values increase with sympathetic neutrally mediated palmar sweating [37]; multiple investigations have shown that acute mental stress is associated with increases in electrical skin conductance values [42][43][44][45][46]. Evidence in the literature shows that decreased BIS values are associated with decreased skin conductance [47].…”

Exposure to intimate partner aggression during childhood is associated with blunted skin conductance recovery following stress in early adulthood

“…However, the distinction between reactive aggression and proactive regression is important: reactive aggression is related to exaggerated SCL reactivity, but proactive aggression is associated with low SCL reactivity (Armstrong et al, 2019). In one of the few studies that has examined trajectories of SCL during a task rather than only averaging SCL, depressed adults exhibited lower mean SCL and lower increases in SCL while working on difficult mathematic problems compared to matched controls (Kim et al, 2019). In sum, anxiety and reactive aggression are related to exaggerated SCL reactivity, whereas depression and proactive aggression are related to low SCL reactivity.…”

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

“…This is because the wearable device can provide the physiological data (i.e., HRV and SC) to complement the data from the smartphone, which are limited only to the behavior (i.e., acceleration and GPS) and social interaction (i.e., call and SMS) of the participants. Among various physical health data from the wearable devices, SC and HRV were scientifically proven to be related to mental health problems [32], [34], [35], [104]. The SC response of the patients with depression has been shown to be lower than that of the persons with strong mental health [104].…”

“…Among various physical health data from the wearable devices, SC and HRV were scientifically proven to be related to mental health problems [32], [34], [35], [104]. The SC response of the patients with depression has been shown to be lower than that of the persons with strong mental health [104]. Additionally, patients with stress and anxiety symptoms were found to display lower HRV than that of the control group [32], [35].…”