Psychological Correlates of Nonspecific Electrodermal Responses

Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls

Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

Electrodermal Activity Is Sensitive to Sleep Deprivation but Does Not Moderate the Effect of Total Sleep Deprivation on Affect

Emotion is characterized by dimensions of affective valence and arousal, either or both of which may be altered by sleep loss, thereby contributing to impaired regulatory functioning. Controlled laboratory studies of total sleep deprivation (TSD) generally show alterations in physiological arousal and affective state, but the relationship of affect and emotion with physiological arousal during TSD has not been well characterized. Established methods for examining physiological arousal include electrodermal activity (EDA) measures such as non-specific skin conductance responses (NSSCR) and skin conductance level (SCL). These measures are robust physiological markers of sympathetic arousal and have been linked to changes in experienced emotion. To explore the link between physiological arousal and affect during sleep deprivation, we investigated individuals’ EDA under TSD and its relationship to self-reported affect. We also investigated the relationship of EDA to two other measures known to be particularly sensitive to the arousal-decreasing effects of TSD, i.e., self-reported sleepiness and performance on a vigilant attention task. Data were drawn from three previously published laboratory experiments where participants were randomly assigned to either well-rested control (WRC) or 38 h of TSD. In this data set, comprising one of the largest samples ever used in an investigation of TSD and EDA (N = 193 with 74 WRC and 119 TSD), we found the expected impairing effects of TSD on self-reported affect and sleepiness and on vigilant attention. Furthermore, we found that NSSCR, but not SCL, were sensitive to TSD, with significant systematic inter-individual differences. Across individuals, the change in frequency of NSSCR during TSD was not predictive of the effect of TSD on affect, sleepiness, or vigilant attention, nor was it related to these outcomes during the rested baseline. Our findings indicate that while physiological arousal, as measured by EDA, may be useful for assessing TSD-related changes in non-specific arousal at the group level, it is not associated with individuals’ self-reported affect at rest nor their change in affect during TSD. This suggests that an essential aspect of the relationship between physiological arousal and self-reported affect is not well captured by EDA as measured by NSSCR.

Influence of ambient temperature on tonic and phasic electrodermal activity components

OBJECTIVE

Electrodermal Activity (EDA) is a reliable indicator for variations in the skin electrical properties attributed to sympathetic nerve system activity. EDA recordings can be influenced by various internal and external factors including environmental ones. Ambient temperature can be considered as one of the possible factors which might influence EDA recordings. Hence, this study aimed to precisely investigate influence of ambient temperature on tonic and phasic EDA components by employing a new EDA measurement technique, which depends on simultaneously recording of several EDA parameters.

APPROACH

Tonic and phasic EDA components during three different ambient temperature levels were recorded from 36 healthy participants. In addition, for evoking electrodermal responses, participants were exposed to cognitive, visual and breathing external stimuli.

MAIN RESULTS

Significant effects of temperature on tonic skin conductance (SC), skin susceptance (SS) and skin potential (SP) were obtained, whereas such significant effects were not observed with phasic SC, SS and SP. Tonic EDA parameters were increased as a function of temperature, but changes in phasic component were fluctuating.

SIGNIFICANCE

This should mean that, keeping recording of tonic EDA component in normal room temperature is highly crucial, but for recording or analysis of phasic component it is not important as they are more robust in this context. This is important in applications of EDA instruments, particularly in wearable devices where environmental temperature typically cannot be controlled.

Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity

Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin’s electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike’s Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA.

Electrodermal activity (EDA) is an indirect read-out of the body’s sympathetic nervous system, or fight-or-flight response, measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Our previous worked showed that the times in between EDA pulses obeyed a specific statistical distribution, the inverse Gaussian, that arises from the physiology of EDA production. In this work, we build on that insight to analyze the amplitudes of EDA pulses. In an analysis of EDA data recorded in 11 healthy volunteers during quiet wakefulness and 11 different healthy volunteers during controlled propofol sedation, we establish that the amplitudes of EDA pulses also have specific statistical structure, as the differences of inverse Gaussians, that arises from the physiology of sweat production. We capture that structure using a series of progressively simpler models that each prioritize different parts of the pulse amplitude distribution. Our findings show that a physiologically-based statistical model provides a parsimonious and accurate description of EDA. This enables increased reliability and robustness in analyzing EDA data collected under any circumstance.

The correlations among the skin conductance features responding to physiological stress stimuli

BACKGROUND/AIM

The skin conductance responses (SCRs) are a well-accepted indicator of physiological arousal for both research purposes and clinical approaches. The shape of SCRs is analyzed by various features. However, the estimation of how much (in %) one feature can explain another is still an open issue. The aim of this study was to assess whether variation in one SCR feature predicts changes in other features.

METHODS

Skin conductance (SC) was measured during relaxation and mental stress in 40 subjects. SCRs were induced by three external stimuli, which were deep breath, a mental arithmetic, task and a visual task.

RESULTS

The findings of this study showed that about 55% (R²  = 0.55) of the variation in the half recovery time (SCRs_rec 50%) can be explained by the rise time (SCRs_ris), whereas variation in amplitude of the skin conductance responses (SCRs_amp) and the skin conductance level (SCL) is independent and cannot be explained by the other features, as R² values obtained from all analyses among these SCR features in average were lower 0.19.

CONCLUSIONS

The study results suggest that the two timing phases (SCRs_rec and SCRs_ris) are not completely independent from each other, although they might be governed by different sweating mechanisms (secretion and reabsorption). However, SCRs_amp and SCL were independent. These findings can help in choosing the optimal set of features of an automated system for processing EDA, which reflect the alterations in the activation level generated during an emotional episode.

A Circuit for Simultaneous Measurements of Skin Electrical Conductance, Susceptance, and Potential

A circuit is presented that enables measurement of skin electrical conductance, susceptance, and potential simultaneously beneath the same monopolar electrode. Example measurements are shown to confirm the function of the circuit. The measurements are also in accordance with earlier findings that changes in skin conductance and potential do not always correspond and hence contain unique information.