An Individualized Approach to Skin Conductance Assessment during Execution of Tasks of Different Complexities.

Researchers use different approaches to analyze changes in skin conductance. At the same time, individual differences observed in both the initial values of skin conductance and response to stimuli do not allow comparing the responses of different subjects to the same impact. An individualized approach to the assessment of psycho-emotional stress has been developed using the real-time skin conductance index during continuous performance of a task with different levels of complexity. The participants, 18 second-year students at the actors' high school, performed a simple task (expressive reading aloud of a simple text) and a difficult task (expressive reading aloud of the text inverted 180°). During the task, the skin conductance in the participants was continuously recorded. We revealed a change in the parameters of skin conductivity depending on the complexity of the task, both in comparison with resting state (baseline) and with simple and complex stages of the study. However, the individual skin conduction curves in the subjects differed greatly. A method for assessing the individual differences in parameters of skin conductance was proposed based on ranking by categories of values during continuous performance of tasks with different levels of complexity. This method allows comparing the levels of cognitive load in participants with different initial levels of skin conductance due to transition from the absolute to relative values at time intervals that are important for the researcher.

Deep Learning Framework for Categorical Emotional States Assessment Using Electrodermal Activity Signals.

In this study, we attempted to classify categorical emotional states using Electrodermal Activity (EDA) signals and a configurable Convolutional Neural Network (cCNN). The EDA signals from the publicly available, Continuously Annotated Signals of Emotion dataset were down-sampled and decomposed into phasic components using the cvxEDA algorithm. The phasic component of EDA was subjected to Short-Time Fourier Transform-based time-frequency representation to obtain spectrograms. These spectrograms were input to the proposed cCNN to automatically learn the prominent features and discriminate varied emotions such as amusing, boring, relaxing, and scary. Nested k-Fold cross-validation was used to evaluate the robustness of the model. The results indicated that the proposed pipeline could discriminate the considered emotional states with a high average classification accuracy, recall, specificity, precision, and F-measure scores of 80.20%, 60.41%, 86.8%, 60.05%, and 58.61%, respectively. Thus, the proposed pipeline could be valuable in examining diverse emotional states in normal and clinical conditions.

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals.

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects' Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention.

Sympathetic and electrochemical skin responses in the assessment of sudomotor function: a comparative study.

OBJECTIVES: The sympathetic skin response (SSR) is a well-established test, whereas the electrochemical skin conductance (ESC) is still under evaluation. Our aim was therefore to assess the diagnostic accuracy of ESC to detect abnormal sudomotor function, using SSR as a reference test. METHODS: A cross sectional observational study was performed of 61 neurological patients assessed for possible sudomotor dysfunction and 50 age-matched healthy controls (HC). Patients with diagnoses of vasovagal syncope (VVS, n=25), Parkinson's disease (PD, n=15), multiple system atrophy (MSA, n=11) and peripheral neuropathies (PN, n=10) were included. Sudomotor function was assessed with SSR and ESC tests in all participants. The absence of SSR in the palms or soles indicates abnormal sudomotor function. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic value of the ESC. Cardiovascular autonomic (CV-Aut) function was evaluated through the Ewing score, based on the following tests: Heart rate change with deep breathing, Valsalva ratio, 30:15 ratio, blood pressure changes on standing and during isometric exercise. A Ewing score ≥ 2 indicates the presence of CV-Aut dysfunction. RESULTS: Mean SSR amplitudes and ESC values showed differences between HC and patients with MSA or PN (p < 0.05), but not in patients with VVS or PD. Absence of SSR was associated with abnormal ESC (p < 0.05). Patients with abnormal CV-Aut dysfunction had lower ESC (p< 0.05). Palm ESC (P-ESC) and sole ESC (S-ESC) assessment had a sensitivity of 0.91 and 0.95 to predict sudomotor dysfunction, with a specificity of 0.78 and 0.85, respectively. The area under ROC curve was 0.905 and 0.98, respectively. CONCLUSIONS: ESC in palms and soles has a high diagnostic accuracy for sudomotor dysfunction as detected by absent SSR in patients with MSA and PN.

Fear learning and extinction predicts anxiety in daily life: a study of Pavlovian conditioning and ecological momentary assessment.

BACKGROUND: The association between anxious mood and aberrant fear learning mechanisms has not been fully elucidated. Studying how fear conditioning and extinction constructs relate to anxiety symptoms and reactivity to stressful and benign moments in everyday life provides a powerful addition to experimental paradigms. METHOD: Fifty-one young adults completed laboratory-based differential conditioning and extinction tasks with (CS + ) and without (CS-) an aversive unconditional stimulus (US). Electrodermal skin conductance responses were measured during each phase, followed by ecological momentary assessment (EMA) tapping anxiety and stressors six times daily for seven days (2, 142 moments). RESULTS: Conditioned electrodermal reactivity to the CS + and overgeneralisation to the CS- were associated with greater change in anxiety (measured via EMA), across non-stressful situations, remaining the same across stressful situations. Likewise, during extinction when the CS + is now safe, more electrodermal reactivity to the CS + was associated with more anxiety change across non-stressful situations and remained the same across stressful situations. Also, during extinction when threat is absent, more electrodermal reactivity at the late stage of the CS- was associated with less momentary anxiety change in response to stressful situations; more electrodermal activity at the late stage of the CS + was associated with more anxiety change across non-stressful situations and remained the same across stressful situations. CONCLUSIONS: Sampling 'in vivo' emotion and stress experiences, study findings revealed links between conditioned electrodermal reactivity and overgeneralisation to safe stimuli and heightened anxious reactivity during non-stressful (i.e. safe) moments in daily life, coupled with less change in response to actual stressors.

The use of electrodermal activity in pulpal diagnosis and dental pain assessment.

AIMS: To explore whether electrodermal activity (EDA) can serve as a complementary tool for pulpal diagnosis (Aim 1) and an objective metric to assess dental pain before and after local anaesthesia (Aim 2). METHODOLOGY: A total of 53 subjects (189 teeth) and 14 subjects (14 teeth) were recruited for Aim 1 and Aim 2, respectively. We recorded EDA using commercially available devices, PowerLab and Galvanic Skin Response (GSR) Amplifier, in conjunction with cold and electric pulp testing (EPT). Participants rated their level of sensation on a 0-10 visual analogue scale (VAS) after each test. We recorded EPT-stimulated EDA activity before and after the administration of local anaesthesia for participants who required root canal treatment (RCT) due to painful pulpitis. The raw data were converted to the time-varying index of sympathetic activity (TVSymp), a sensitive and specific parameter of EDA. Statistical analysis was performed using Python 3.6 and its Scikit-post hoc library. RESULTS: Electrodermal activity was upregulated by the stimuli of cold and EPT testing in the normal pulp. TVSymp signals were significantly increased in vital pulp compared to necrotic pulp by both cold test and EPT. Teeth that exhibited intensive sensitivity to cold with or without lingering pain had increased peak numbers of TVSymp than teeth with mild sensation to cold. Pre- and post-anaesthesia EDA activity and VAS scores were recorded in patients with painful pulpitis. Post-anaesthesia EDA signals were significantly lower compared to pre-anaesthesia levels. Approximately 71% of patients (10 of 14 patients) experienced no pain during treatment and reported VAS score of 0 or 1. The majority of patients (10 of 14) showed a reduction of TVSymp after the administration of anaesthesia. Two of three patients who experienced increased pain during RCT (post-treatment VAS > pre-treatment VAS) exhibited increased post-anaesthesia TVSymp. CONCLUSIONS: Our data show promising results for using EDA in pulpal diagnosis and for assessing dental pain. Whilst our testing was limited to subjects who had adequate communication skills, our future goal is to be able to use this technology to aid in the endodontic diagnosis of patients who have limited communication ability.

ComEDA: A new tool for stress assessment based on electrodermal activity.

Non-specific sympathetic arousal responses to different stressful elicitations can be easily recognized from the analysis of physiological signals. However, neural patterns of sympathetic arousal during physical and mental fatigue are clearly not unitary. In the context of physiological monitoring through wearable and non-invasive devices, electrodermal activity (EDA) is the most effective and widely used marker of sympathetic activation. This study presents ComEDA, a novel approach for the characterization of complex dynamics of EDA. ComEDA overcomes the methodological limitations related to the application of nonlinear analysis to EDA dynamics, is not parameter-sensitive and is suitable for the analysis of ultra-short time series. We validated the proposed algorithm using synthetic series of white noise and 1/f noise, varying the number of samples from 50 to 5000. By applying our approach, we were able to discriminate a statistically significant increase of complexity in the 1/f noise with respect to white noise, obtaining p-values in the range [4.35 × 10-6, 0.03] after the Mann-Whitney test. Then, we tested ComEDA on both EDA signal and its tonic and phasic components, acquired from healthy subjects during four experimental protocols: two inducing a sympathetic activation through physical efforts and two based on mentally stressful tasks. Results are encouraging and promising, outperforming state of the art metrics such as the Sample Entropy. ComEDA shows good performance not only in discriminating between stressful tasks and resting state (p-value < 0.01 after the Wilcoxon non-parametric statistical test applied to EDA signals of all the four datasets), but also in differentiating different trends of complexity of EDA dynamics when induced by physical and mental stressors. These findings suggest future applications to automatically detect and selectively identify threats due to overwhelming stress impacting both physical and mental health or in the field of telemedicine to monitor autonomic diseases correlated to atypical sympathetic activation. The Matlab code implementing the ComEDA algorithm is available online.

Measuring emotional variables in occupational performance: A scoping review.

BACKGROUND: As interest in job-related psychology increased, the need to focus on understanding workplace stress was emphasized. Negative emotional states such as anxiety and stress permeate the organization and, if uncontrolled, can negatively impact the health and work performance of workers. Therefore, attempts to analyze various signals to understand human emotional states or attitudes may be important for future technological development. OBJECTIVE: The purpose of this study was to identify what biological variables can discriminate emotions that can significantly affect work results. METHODS: Databases (Embase, PsychINFO, PubMed, and CINAHL) were searched for all relevant literature published as of December 31, 2019. RESULTS: Brain activity (BA) and heart rate (HR) or heart rate variability (HRV) are adequate for assessing negative emotions, while BA, galvanic skin response (GSR), and salivary samples (SS) can confirm positive and negative emotions. CONCLUSION: In the future, researchers should study measurement tools and bio-related variables while workers perform tasks and develop intervention strategies to address emotions associated with work. This may enable workers to perform tasks more efficiently, prevent accidents, and satisfy clients.

Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning.

OBJECTIVE: Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of children's self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable ofdiscriminating clinically significant pain from clinically nonsignificant pain in children.

Assessment of diabetic small-fiber neuropathy by using short-wave infrared hyperspectral imaging.

Among patients with type 2 diabetes mellitus (T2DM), the association between hyperspectral imaging (HSI) examination and diabetic neuropathy (DN) is ascertained using HSI of the feet using four types of spectral difference measurements. DN was evaluated by traditional Michigan Neuropathy Screening Instrument (MNSI), evaluation of painful neuropathy (ID-Pain, DN4) and sudomotor function by measuring electrochemical skin conductance (ESC). Of the 120 T2DM patients and 20 healthy adults enrolled, T2DM patients are categorized into normal sudomotor (ESC >60 µS) and sudomotor dysfunction (ESC ≤ 60 µS) groups. Spectral difference analyses reveal significant intergroup differences, whereas traditional examinations cannot distinguish between the two groups. HSI waveform reflectance gradually increases with disease severity, at 1400 to 1600 nm. The area under the curve (AUC) of receiver operating characteristic (ROC) analysis for abnormal ESC is ≥0.8 for all four HSI methods. HSI could be an objective, sensitive, rapid, noninvasive and remote approach to identify early small-fiber DN.

The effect of social anxiety on the acquisition and extinction of low-cost avoidance.

Excessive avoidance and safety behaviours are a hallmark feature of social anxiety disorder. However, the conditioning and extinction of avoidance behaviour in social anxiety is understudied. Here, we examined the effect of individual differences in social anxiety on low-cost operant avoidance conditioning and extinction in 80 female participants. We employed an avoidance conditioning and extinction paradigm and measured skin conductance response, threat expectancy ratings and avoidance behaviour throughout the task. Findings demonstrated that elevated levels of social anxiety predicted the generalisation of conditioned avoidance responses across to safety cues during avoidance conditioning. When the opportunity to avoid was returned after the threat extinction phase, elevated social anxiety was associated with increased avoidance behaviour to threat cues. The results suggest that compromised extinction of avoidance behaviour is a characteristic of social anxiety and supports the strategy of minimising avoidance and safety behaviours during exposure therapy for the treatment of social anxiety disorder. Future research should utilise the avoidance conditioning and extinction paradigm as a laboratory model for clinical research to investigate how, and under what circumstances, the extinction of avoidance and safety behaviours can be improved for individuals high in social anxiety.

Assessment of mental fatigue and stress on electronic sport players with data fusion.

Stress and mental fatigue are in existence constantly in daily life, and decrease our productivity while performing our daily routines. The purpose of this study was to analyze the states of stress and mental fatigue using data fusion while e-sport activity. In the study, ten volunteers performed e-sport duty which required both physical and mental effort and skills for 2 min. Volunteers' electroencephalogram (EEG), galvanic skin response (GSR), heart rate variability (HRV), and eye tracking data were obtained before and during game and then were analyzed. In addition, the effects of e-sports were evaluated with visual analogue scale and d2 attention tests. The d2 tests are performed after the game, and the game has a positive effect on attention and concentration. EEG from the frontal region indicates that the game is partly caused by stress and mental fatigue. HRV analysis showed that the sympathetic and vagal activities created by e-sports on people are different. By evaluating HRV and GSR together, it was seen that the emotional processes of the participants were stressed in some and excited in others. Data fusion can serve a variety of purposes such as determining the effect of e-sports activity on the person and the appropriate game type.

Assessment of sympathetic sudomotor function in amyotrophic lateral sclerosis with electrochemical skin conductance.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is now recognized as a multisystem neurodegenerative disorder, comprising autonomic dysfunction. We aimed to assess sudomotor function in ALS by measuring the electrochemical skin conductance (ESC). METHODS: Thirty-one ALS patients [median age of 62 years (1st-3rd interquartile range - IQR, 56-72), male 71%] were prospectively compared with 29 healthy controls, matched for age and sex. We analysed ESC results from hands and feet, bilaterally. RESULTS: A total of 120 ESC recordings were obtained. Hands and feet ESC measurements were significantly lower in patients compared with controls [64 µS (1st-3rd IQR, 57-58) versus 78 µS (1st-3rd IQR, 70.5-84), p < 0.001 and 76 µS (1st-3rd IQR, 68-83) versus 81 µS (1st-3rd IQR, 78-86), p = 0.008, respectively]. In ALS group, no differences were observed between spinal and bulbar-onset forms for hands and feet results (p > 0.05). Hands and feet ESC measurements did not correlate also with disease duration, total ALSFRS-R scale, or ALSFRS-R progression rate (all p > 0.05). CONCLUSION: ESC is a non-invasive, fast and quantitative method suitable for assessing sudomotor function. ALS patients revealed a decreased function in upper and lower extremities. SIGNIFICANCE: Sudomotor dysfunction is part of the ALS manifestations.

Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity.

An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the United States. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerical or visual analog scale (VAS). This subjectivity complicates pain management and exacerbates the epidemic of opioid abuse. We have tested classification and regression machine learning models to objectively estimate pain sensation in healthy subjects using electrodermal activity (EDA). Twenty-three volunteers underwent pain stimulation using thermal grills. Three different "pain stimulation intensities" were induced for each subject, who reported the "pain sensation" right after each stimulus using a VAS (0-10). EDA data were collected throughout the experiment. For machine learning, we computed validated features of EDA based on time-domain decomposition, spectral analysis, and differential features. Models for estimation of pain stimulation intensity and pain sensation achieved maximum macroaveraged geometric mean scores of 69.7% and 69.2%, respectively, when three classes were considered ("No," "Low," and "High"). Regression of levels of stimulation intensity and pain sensation achieved R2 values of 0.357 and 0.47, respectively. Overall, the high variance and inconsistency of VAS scores led to lower performance of pain sensation classification, but regression was better for pain sensation than stimulation intensity. Our results provide that three levels of pain can be quantified with good accuracy and physiological evidence that sympathetic responses recorded by EDA are more correlated to the applied stimuli's intensity than to the pain sensation reported by the subject.

Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study.

BACKGROUND: Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects. OBJECTIVE: The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients. METHODS: The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models. RESULTS: Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier. CONCLUSIONS: We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/17783.

Assessment of Resilience of the Hellenic Navy Seals by Electrodermal Activity during Cognitive Tasks.

Stress resilience plays a key role in task performance during emergencies, especially in occupations like military special forces, with a routine consisting of unexpected events. Nevertheless, reliable and applicable measurements of resilience in predicting task performance in stressful conditions are still researched. This study aimed to explore the stress response in the Hellenic Navy SEALs (HN-SEALs), using a cognitive-physiological approach. Eighteen candidates under intense preparation for their enlistment in the HN-SEALs and 16 healthy controls (HCs) underwent Stroop tests, along with mental-state and personality examination. Simultaneously, electrodermal activity (EDA) was assessed during each one of cognitive testing procedures. Compared to healthy control values, multiple components of EDA values were found decreased (p < 0.05) in the HN-SEALs group. These results were associated with an increase in resilience level in the HN-SEALs group, since a restricted sympathetic reactivity according to the reduced EDA values was observed during the stressful cognitive testing. This is the first report providing physiological measurements of the sympathetic response of HN-SEALs to a stressful situation and suggests that EDA turns out to be a simple and objective tool of sympathetic activation and it may be used as a complementary index of resilience in HN-SEALs candidates.

Does the assessment of different combinations of within-phase subjective measures influence electrodermal responding and between-phase subjective ratings during fear conditioning and extinction experiments?

The type and number of dependent measures assessed have varied between fear conditioning and extinction experiments and it remains unclear whether methodological differences influence results. Measuring skin conductance responses (SCRs) and between-phase subjective ratings between four conditions; Con condition had no within-phase ratings; US Exp condition included within-phase US expectancy ratings only; CS Eval condition included within-phase CS evaluations only and All Meas condition included both. All Meas condition exhibited larger SCRs compared to other conditions during acquisition, extinction, and return of differential responding at test. Differential SCRs did not extinguish in CS Eval and Con conditions, and CS Eval condition exhibited smaller CS- SCRs than other conditions throughout phases. US Exp condition revealed differential conditioning, successful extinction, and no return of differential SCRs. Between-phase ratings were not affected by within-phase ratings. Researchers should consider assessing different combinations of within-phase subjective ratings depending on the aims of their research.

Race and ethnic variation in college students' allostatic regulation of racism-related stress.

Racism-related stress is thought to contribute to widespread race/ethnic health inequities via negative emotion and allostatic stress process up-regulation. Although prior studies document race-related stress and health correlations, due to methodological and technical limitations, they have been unable to directly test the stress-reactivity hypothesis in situ. Guided by theories of constructed emotion and allostasis, we developed a protocol using wearable sensors and daily surveys that allowed us to operationalize and time-couple self-reported racism-related experiences, negative emotions, and an independent biosignal of emotional arousal. We used data from 100 diverse young adults at a predominantly White college campus to assess racism-related stress reactivity using electrodermal activity (EDA), a biosignal of sympathetic nervous system activity. We find that racism-related experiences predict both increased negative emotion risk and heightened EDA, consistent with the proposed allostatic model of health and disease. Specific patterns varied across race/ethnic groups. For example, discrimination and rumination were associated with negative emotion for African American students, but only interpersonal discrimination predicted increased arousal via EDA. The pattern of results was more general for Latinx students, for whom interpersonal discrimination, vicarious racism exposure, and rumination significantly modulated arousal. As with Latinx students, African students were particularly responsive to vicarious racism while 1.5 generation Black students were generally not responsive to racism-related experiences. Overall, these findings provide support for allostasis-based theories of mental and physical health via a naturalistic assessment of the emotional and sympathetic nervous system responding to real-life social experiences.

Electrodermal Activity and Stress Assessment.

Stress, as a kind of emotion, is inevitable in everyday life. In psychosomatic medicine stress represents a powerful link in the pathophysiological chain of disorder. Having evidence about the power of stress on the body, the interest in medicine was how to measure it in appropriate, fast way and with minimal cost. Electrodermal activity seems to be available for this purpose. The galvanic skin response (GSR) is an objective, transient indication of autonomic nervous system arousal in response to a stimulus. It refers to changes in sweat gland activity that are reflective of the intensity of our emotional arousal. In this article we discuss physiological specifics of skin conductance/resistance and how it is measured in practice. The most used application of GSR is in biofeedback methodology. Biofeedback assessment and training exactly uses skin reaction to different stimuli and aims to gain voluntary control over this autonomic response. The aim of this article is to show effectiveness of this method in paediatric practice.

Mother-father physiological synchrony during conflict and moderation by parenting challenges: Findings from parents of children with autism spectrum disorder.

INTRODUCTION: Despite robust evidence in the broader family process literature for within-couple associations between romantic partners' physiological responding, this linkage has not been tested directly among parents raising a child with autism spectrum disorder (ASD). Explicit attention to these parents is warranted on the basis of the established family-wide challenges associated with the persistent emotional, functional, and behavior impairments typically observed in children with ASD (see Karst & Van Hecke, 2012). The first purpose of this study is to extend examination of within-couple associations between electrodermal activity (EDA) to mothers and fathers of children with ASD. The second purpose is to test 2 potential moderators of EDA synchrony reflecting heightened challenges among parents of children with ASD, namely parenting as an observed conflict topic and global parenting stress. METHOD: Both parents wore wrist sensors that captured continuous EDA during a recorded marital conflict interaction. Trained coders provided ratings of emotional intensity and conflict topics, including whether the topic of parenting was discussed. Parents reported their global level of parenting stress. Dyadic multilevel modeling was used to test our hypotheses and accounted for interaction factors (e.g., movement data, emotional intensity). RESULTS: Findings identified significant positive associations between parents' EDA. One moderator result emerged; specifically, father EDA was a stronger predictor of mother EDA when parenting was discussed as a conflict topic. CONCLUSION: This study extends evidence of within-couple physiological linkage to parents raising a child with ASD and provides a foundation for future research examining the family-wide implications of this linkage. (PsycInfo Database Record (c) 2021 APA, all rights reserved).