Pre-pandemic electrodermal activity predicts current COVID-related fears: household size during lockdown as a moderating factor

Background: Despite the immense impact of COVID-19 on mental health, there is a lack of prospective studies examining physiological predictors of current risk factors. Moreover, although physiological processes evidently interact with socio-demographic factors to modulate individuals' response to a crisis, it remains largely unknown how these complex interactions shape people's mental responses to COVID-19. To fill these gaps of knowledge, we chose a potent physiological marker of distress - heightened baseline electrodermal activity (EDA) measured before the pandemic began - and hypothesized it would be related to greater COVID-related fears and worries as a function of individuals' household size.Method: 185 individuals (71% women), who had participated in our lab studies 2-3 years ago, in which we assessed their baseline EDA, completed several questionnaires online, including assessments of their current fears regarding COVID. Participants also reported the number of people in their household, with whom they had been together during a lockdown which was taking place at the time. We used pre-pandemic EDA measures in combination with their household size to predict participants' current fears.Results: Pre-pandemic EDA measures predicted current COVID-related fears and worries. Specifically for the EDA measure "number of skin conductance responses", we further found that the number of people in the household during the lockdown, moderated the abovementioned relationship, such that it occurred in individuals with average and larger households and not in those with small households.Conclusions: We provide a highly relevant and unique combination of physiological, socio-demographic, and psychological measures, which augments the potential to optimally target populations vulnerable to COVID-related distress, and subsequently offer them early mental health interventions.

The application of sudoscan for screening microvascular complications in patients with type 2 diabetes

The aim of the study was to evaluate the performance of sudoscan in screening diabetic microvascular complications in patients with type 2 diabete mellitus (T2DM). 515 patients with T2DM aged from 23 to 89 years were included for analysis in our study. The mean age was 60.00 ± 11.37 years and the mean duration of T2DM was 8.44 ± 7.56 years. Electrochemical skin conductance (ESC) in hands and feet was evaluated by SUDOCAN. Diabetic peripheral neuropathy (DPN) was diagnosed in 378 patients (44.3%), diabetic kidney disease (DKD) in 161 patients (31.26%), diabetic retinopathy (DR) in 148 patients (28.74%). Hands and feet ESC was significantly and independently associated with the presence of DPN, DKD and DR. Patients with a lower ESC (<60 µS) had 5.63-fold increased likelihood of having DPN, 4.90-fold increased likelihood of having DKD, 1.01-fold increased likelihood of having DR, than those with a higher ESC. Age, duration of T2DM, smoking, renal function and vibration perception thresholds were negatively correlated with ESC. Sudoscan parameters were correlated with diabetic microvascular complications, especially with DPN. Sudoscan could be an effective screening tool in primary health care for early screening microvascular complications.

Cortical and autonomic responses during staged Taoist meditation: Two distinct meditation strategies

Meditation is a consciousness state associated with specific physiological and neural correlates. Numerous investigations of these correlates reported controversial results which prevented a consistent depiction of the underlying neurophysiological processes. Here we investigated the dynamics of multiple neurophysiological indicators during a staged meditation session. We measured the physiological changes at rest and during the guided Taoist meditation in experienced meditators and naive subjects. We recorded EEG, respiration, galvanic skin response, and photoplethysmography. All subjects followed the same instructions split into 16 stages. In the experienced meditators group we identified two subgroups with different physiological markers dynamics. One subgroup showed several signs of general relaxation evident from the changes in heart rate variability, respiratory rate, and EEG rhythmic activity. The other subgroup exhibited mind concentration patterns primarily noticeable in the EEG recordings while no autonomic responses occurred. The duration and type of previous meditation experience or any baseline indicators we measured did not explain the segregation of the meditators into these two groups. These results suggest that two distinct meditation strategies could be used by experienced meditators, which partly explains the inconsistent results reported in the earlier studies evaluating meditation effects. Our findings are also relevant to the development of the high-end biofeedback systems.

A Shrewd Artificial Neural Network-Based Hybrid Model for Pervasive Stress Detection of Students Using Galvanic Skin Response and Electrocardiogram Signals

Mental illness issues are a very common health issue in youths and adults across the world. The usage of real-time data analytics in health care has a great potential to improve and enhance the quality of health care services, including diagnosis and medical prescription. Stress is one of the major health issues these days, which leads to many acute and sometimes incurable diseases to the students of very young age. Stress affects physiological parameters of the human body; due to these, human emotions may also change. This research paper proposes a hybrid model for pervasive stress detection, which deals with imbalance class problems using real-time data analytics and Internet of Things and it also presents a new stress analysis system to detect stressful conditions of the student, and to diagnose whether they are stressed or relaxed by using a designed set of experimental tasks. Regular monitoring of students'/professionals' health, including measurement of Galvanic Skin Response (GSR) and Electrocardiogram (ECG) data, provides a good understanding of their stress level. Data are acquired by using GSR and ECG sensors for 34 participants while undertaking five different tasks discussed in the proposed experiment. The graphical relationship between heart rate, blood pressure, and skin conductance across various experimental activities highlights the fact as to how physiological parameters of the human body get affected along with the mental status of the students. This article performs accuracy computation by using different machine-learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Bagging Classifiers (BAG), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network (ANN) followed by tuning with the best set of hyper parameters for each model. The proposed hybrid classification model deals with the class imbalance problem by using the Synthetic Minority Oversampling Technique. The shrewd ANN-based hybrid model achieves 99.4% accuracy on the self-generated dataset for the mental state classification of the students, which is best among other classifiers such as LR, SVM, KNN, BAG, RF, GB, and ANN. The prediction result of all 34 participants of the experiment is also classified into four categories: relaxed, stressed, partially stressed, and happy.

Pen-Type Electrodermal Activity Sensing System for Stress Detection Based on Likelihood Ratios

Psychological stress experienced during academic testing is a significant performance factor for some students. While a student may be able to recognize and self-report exam stress, unobtrusive tools to track stress in real time and in association with specific test problems are lacking. This effort pursued the design and initial assessment of an electrodermal activity (EDA) sensor mounted to a pen/pencil 'trainer:' a holder into which a pen/pencil is inserted that can help a person learn how to properly grip a writing instrument. This small assembly was held in the hand of each subject during early experiments and can be used for follow-on, mock test-taking scenarios. In these experiments, data were acquired with this handheld device for each of 36 subjects (Kansas State University Internal Review Board Protocol #9864) while they viewed approximately 30 minutes of emotion-evoking videos. Data collected by the EDA sensor were analyzed by an EDA signal processing app, which calculated and stored parameters associated with significant phasic EDA peaks while allowing intermediate peak detection processes to be visualized. These peak data were then subjected to a hypothesis driven stress-detection test that employed likelihood ratios to identify 'relaxed' versus 'stressed' events. For these initial testing scenarios, which were free of hand motions, this pen-type EDA sensing system discerned 'relaxed' versus 'stressed' phasic responses with 87.5% accuracy on average, where subject self-assessments of perceived stress levels were used to establish ground truth.

Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review

There is a strong increase in the use of devices that measure physiological arousal through electrodermal activity (EDA). Although there is a long tradition of studying emotions during learning, researchers have only recently started to use EDA to measure emotions in the context of education and learning. This systematic review aimed to provide insight into how EDA is currently used in these settings. The review aimed to investigate the methodological aspects of EDA measures in educational research and synthesize existing empirical evidence on the relation of physiological arousal, as measured by EDA, with learning outcomes and learning processes. The methodological results pointed to considerable variation in the usage of EDA in educational research and indicated that few implicit standards exist. Results regarding learning revealed inconsistent associations between physiological arousal and learning outcomes, which seem mainly due to underlying methodological differences. Furthermore, EDA frequently fluctuated during different stages of the learning process. Compared to this unimodal approach, multimodal designs provide the potential to better understand these fluctuations at critical moments. Overall, this review signals a clear need for explicit guidelines and standards for EDA processing in educational research in order to build a more profound understanding of the role of physiological arousal during learning.

Development of an IoT-Based Sleep Apnea Monitoring System for Healthcare Applications

Sleep is an essential and vital element of a person's life and health that helps to refresh and recharge the mind and body of a person. The quality of sleep is very important in every person's lifestyle, removing various diseases. Bad sleep is a big problem for a lot of people for a very long time. People suffering from various diseases are dealing with various sleeping disorders, commonly known as sleep apnea. A lot of people die during sleep because of uneven body changes in the body during sleep. On that note, a system to monitor sleep is very important. Most of the previous systems to monitor sleeping problems cannot deal with the real time sleeping problem, generating data after a certain period of sleep. Real-time monitoring of sleep is the key to detecting sleep apnea. To solve this problem, an Internet of Things- (IoT-) based real-time sleep apnea monitoring system has been developed. It will allow the user to measure different indexes of sleep and will notify them through a mobile application when anything odd occurs. The system contains various sensors to measure the electrocardiogram (ECG), heart rate, pulse rate, skin response, and SpO2 of any person during the entire sleeping period. This research is very useful as it can measure the indexes of sleep without disturbing the person and can also show it in the mobile application simultaneously with the help of a Bluetooth module. The system has been developed in such a way that it can be used by every kind of person. Multiple analog sensors are used with the Arduino UNO to measure different parameters of the sleep factor. The system was examined and tested on different people's bodies. To analyze and detect sleep apnea in real-time, the system monitors several people during the sleeping period. The results are displayed on the monitor of the Arduino boards and in the mobile application. The analysis of the achieved data can detect sleep apnea in some of the people that the system monitored, and it can also display the reason why sleep apnea happens. This research also analyzes the people who are not in the danger of sleeping problems by the achieved data. This paper will help everyone learn about sleep apnea and will help people detect it and take the necessary steps to prevent it.

The effects of traveling in different transport modes on galvanic skin response (GSR) as a measure of stress: An observational study

BACKGROUND

Stress is one of many ailments associated with urban living, with daily travel a potential major source. Active travel, nevertheless, has been associated with lower levels of stress compared to other modes. Earlier work has relied on self-reported measures of stress, and on study designs that limit our ability to establish causation.

OBJECTIVES

To evaluate effects of daily travel in different modes on an objective proxy measure of stress, the galvanic skin response (GSR).

METHODS

We collected data from 122 participants across 3 European cities as part of the Physical Activity through Sustainable Transport Approaches (PASTA) study, including: GSR measured every minute alongside confounders (physical activity, near-body temperature) during three separate weeks covering 3 seasons; sociodemographic and travel information through questionnaires. Causal relationships between travel in different modes (the "treatment") and stress were established by using a propensity score matching (PSM) approach to adjust for potential confounding and estimating linear mixed models (LMM) with individuals as random effects to account for repeated measurements. In three separate analyses, we compared GSR while cycling to not cycling, then walking to not walking then motorized (public or private) travel to any activity other than motorized travel.

RESULTS

Depending on LMM formulations used, cycling reduces 1-minute GSR by 5.7% [95% CI: 2.0-16.9%] to 11.1% [95% CI: 5.0-24.4%] compared to any other activity. Repeating the analysis for other modes we find that: walking is also beneficial, reducing GSR by 3.9% [95% CI: 1.4-10.7%] to 5.7% [95% CI: 2.6-12.3%] compared to any other activity; motorized mode (private or public) in reverse increases GSR by up to 1.1% [95% CI: 0.5-2.9%].

DISCUSSION

Active travel offers a welcome way to reduce stress in urban dwellers' daily lives. Stress can be added to the growing number of evidence-based reasons for promoting active travel in cities.

Decoding a Music-Modulated Cognitive Arousal State using Electrodermal Activity and Functional Near-infrared Spectroscopy Measurements

Biofeedback systems sense different physiological activities and help with gaining self-awareness. Understanding music's impact on the arousal state is of great importance for biofeedback stress management systems. In this study, we investigate a cognitive-stress-related arousal state modulated by different types of music. During our experiments, each subject was presented with neurological stimuli that elicit a cognitive-stress-related arousal response in a working memory experiment. Moreover, this cognitive-stress-related arousal was modulated by calming and vexing music played in the background. Electrodermal activity and functional near-infrared spectroscopy (fNIRS) measurements both contain information related to cognitive arousal and were collected in our study. By considering various fNIRS features, we selected three features based on variance, root mean square, and local fNIRS peaks as the most informative fNIRS observations in terms of cognitive arousal. The rate of neural impulse occurrence underlying EDA was taken as a binary observation. To retain a low computational complexity for our decoder and select the best fNIRS-based observations, two features were chosen as fNIRS-based observations at a time. A decoder based on one binary and two continuous observations was utilized to estimate the hidden cognitive-stress-related arousal state. This was done by using a Bayesian filtering approach within an expectation-maximization framework. Our results indicate that the decoded cognitive arousal modulated by vexing music was higher than calming music. Among the three fNIRS observations selected, a combination of observations based on root mean square and local fNIRS peaks resulted in the best decoded states for our experimental settings. This study serves as a proof of concept for utilizing fNIRS and EDA measurements to develop a low-dimensional decoder for tracking cognitive-stress-related arousal levels.

Time-varying Spectral Index of Electrodermal Activity to Predict Central Nervous System Oxygen Toxicity Symptoms in Divers: Preliminary results

The most effective method to mitigate decompression sickness in divers is hyperbaric oxygen (HBO₂) pre-breathing. However, divers breathing HBO₂ are at risk for developing central nervous system oxygen toxicity (CNS-OT), which can manifest as symptoms that might impair a diver's performance, or cause more serious symptoms like seizures. In this study, we have collected electrodermal activity (EDA) signals in fifteen subjects at elevated oxygen partial pressures (2.06 ATA, 35 FSW) in the "foxtrot" chamber pool at the Duke University Hyperbaric Center, while performing a cognitive stress test for up to 120 minutes. Specifically, we have computed the time-varying spectral analysis of EDA (TVSymp) as a tool for sympathetic tone assessment and evaluated its feasibility for the prediction of symptoms of CNS-OT in divers. The preliminary results show large increase in the amplitude TVSymp values derived from EDA recordings ~2 minutes prior to expert human adjudication of symptoms related to oxygen toxicity. An early detection based on TVSymp might allow the diver to take countermeasures against the dire consequences of CNS-OT which can lead to drowning.Clinical Relevance-This study provides a sensitive analysis method which indicates a significant increase in the electrodermal activity prior to human expert adjudication of symptoms related to CNS-OT.

Emotion recognition from multimodal physiological measurements based on an interpretable feature selection method

Many studies in literature successfully use classification algorithms to classify emotions by means of physiological signals. However, there are still important limitations in interpretability of the results, i.e. lack of feature specific characterizations for each emotional state. To this extent, our study proposes a feature selection method that allows to determine the most informative subset of features extracted from physiological signals by maintaining their original dimensional space. Results show that features from the galvanic skin response are confirmed to be relevant in separating the arousal dimension, especially fear from happiness and relaxation. Furthermore, the average and the median value of the galvanic skin response signal together with the ratio between SD1 and SD2 from the Poincarè analysis of the electrocardiogram signal, were found to be the most important features for the discrimination along the valence dimension. A Linear Discriminant Analysis model using the first ten features sorted by importance, as defined by their ability to discriminate emotions with a bivariate approach, led to a three-class test accuracy in discriminating happiness, relaxation and fear equal to 72%, 67% and 89% respectively.Clinical relevance This study demonstrates the ability of physiological signals to assess the emotional state of different subjects, by providing a fast and efficient method to select most important indexes from the autonomic nervous system. The approach has high clinical relevance as it could be extended to assess other emotional states (e.g. stress and pain) characterizing pathological states such as post traumatic stress disorder and depression.

Unsupervised Machine Learning Methods for Artifact Removal in Electrodermal Activity

Artifact detection and removal is a crucial step in all data preprocessing pipelines for physiological time series data, especially when collected outside of controlled experimental settings. The fact that such artifact is often readily identifiable by eye suggests that unsupervised machine learning algorithms may be a promising option that do not require manually labeled training datasets. Existing methods are often heuristic-based, not generalizable, or developed for controlled experimental settings with less artifact. In this study, we test the ability of three such unsupervised learning algorithms, isolation forests, 1-class support vector machine, and K-nearest neighbor distance, to remove heavy cautery-related artifact from electrodermal activity (EDA) data collected while six subjects underwent surgery. We first defined 12 features for each halfsecond window as inputs to the unsupervised learning methods. For each subject, we compared the best performing unsupervised learning method to four other existing methods for EDA artifact removal. For all six subjects, the unsupervised learning method was the only one successful at fully removing the artifact. This approach can easily be expanded to other modalities of physiological data in complex settings.Clinical Relevance- Robust artifact detection methods allow for the use of diverse physiological data even in complex clinical settings to inform diagnostic and therapeutic decisions.

A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning

The electrodermal activity (EDA) signal is a sensitive and non-invasive surrogate measure of sympathetic function. Use of EDA has increased in popularity in recent years for such applications as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; diagnosis of depression and epilepsy; and other uses. Recently, there have been several studies using ambulatory EDA recordings, which are often quite useful for analysis of many physiological conditions. Because ambulatory monitoring uses wearable devices, EDA signals are often affected by noise and motion artifacts. An automated noise and motion artifact detection algorithm is therefore of utmost importance for accurate analysis and evaluation of EDA signals. In this paper, we present machine learning-based algorithms for motion artifact detection in EDA signals. With ten subjects, we collected two simultaneous EDA signals from the right and left hands, while instructing the subjects to move only the right hand. Using these data, we proposed a cross-correlation-based approach for non-biased labeling of EDA data segments. A set of statistical, spectral and model-based features were calculated which were then subjected to a feature selection algorithm. Finally, we trained and validated several machine learning methods using a leave-one-subject-out approach. The classification accuracy of the developed model was 83.85% with a standard deviation of 4.91%, which was better than a recent standard method that we considered for comparison to our algorithm.

Female-male Differences Should be Considered in Physical Pain Quantification based on Electrodermal Activity: Preliminary Study

Objective pain quantification is an important but difficult goal. Electrodermal activity (EDA) has been widely explored for this purpose, given its reported sensitivity to pain. However, cognitive stress can hinder successful estimation of physical pain when using EDA signals. We collected EDA signals from ten subjects (5 male and 5 female) undergoing pain stimulation, and calculated phasic, tonic, and frequency-domain features. Each subject experienced pain with and without stress. Three low and three high pain sessions were induced using two thermal grills (low-level for visual analog scale [VAS] 4 or 5 and high-level for VAS 7 or more). The Stroop test was performed for inducing cognitive stress. Significant differences between EDA features of painless and pain segments were observed. Significant differences between no pain and stress were also observed. Furthermore, we compared differences in EDA features between females and males under pain and cognitive stress. Frequency-domain EDA features of pain increased with stress for both females and males. Frequency-domain features derived from females also showed higher standard deviation than did those derived from males. We performed machine learning analysis and evaluated the models using leave-one-subject-out cross-validation. We obtained balanced accuracies of 63.5%, 72.4%, and 53.2% (combined, male, and female) when using training data of the same sex and 47.6%, 57.4%, and 42.7% (combined, male, and female) when using different sex for training.Clinical Relevance-Our preliminary results suggest that sex of patients should be considered to increase the accuracy of pain quantification based on EDA in the presence of cognitive stress.

Validation of Spectral Indices of Electrodermal Activity with a Wearable Device

Electrodermal activity (EDA) has been found to be a highly sensitive, accurate and non-invasive measure of the sympathetic nervous system's activity and has been used to extract biomarkers of various pathophysiological conditions including stress, fatigue, epilepsy, and chronic pain. Recently, various robust signal processing techniques have been developed to obtain more reliable and accurate indices that capture the meaningful characteristics of the EDA using data collected from laboratory-scale devices. However, EDA also has the potential to monitor such physiological conditions in active ambulatory settings, for which the developed tools must be deployed in wearable devices. In this paper, we studied the feasibility of obtaining the highly-sensitive spectral indices of EDA using a wearable device. EDA signals were collected from left hand fingers using a wearable device and a laboratory-scale reference device, while N=18 subjects underwent the Head up Tilt test and the Stroop test to stimulate orthostatic and cognitive stress, respectively. We computed two time-domain indices, the skin conductance level (SCL) and nonspecific skin conductance responses (NS.SCRs), and two spectral indices, the normalized sympathetic components of the EDA (EDASympn), and the time-varying EDA index of sympathetic control (TVSymp). The results showed similar performances for EDASympn and TVSymp indices across both devices. While spectral indices obtained from both devices performed similarly in response to orthostatic and cognitive stress, time-domain exhibited large variation when obtained by the wearable device. Further research is required to develop and refine such devices, as well as the indices used to analyze EDA results.Clinical Relevance- This study proves the feasibility of obtaining spectral indices of EDA using a wearable device, which can be used to develop wearable tools to detect pain, stress, fatigue, between others.

Discriminating Stress From Cognitive Load Using Contactless Thermal Imaging Devices

This study proposes long wave infrared technology as a contactless alternative to wearable devices for stress detection. To this aim, we studied the change in facial thermal distribution of 17 healthy subjects in response to different stressors (Stroop Test, Mental Arithmetic Test). During the experimental sessions the electrodermal activity (EDA) and the facial thermal response were simultaneously recorded from each subject. It is well known from the literature that EDA can be considered a reliable marker for the psychological state variation, therefore we used it as a reference signal to validate the thermal results. Statistical analysis was performed to evaluate significant differences in the thermal features between stress and non-stress conditions, as well as between stress and cognitive load. Our results are in line with the outcomes of previous studies and show significant differences in the temperature trends over time between stress and resting conditions. As a new result, we found that the mean temperature changes of some less studied facial regions, e.g., the right cheek, are able not only to significantly discriminate between resting and stressful conditions, but also allow to recognize the typology of stressors. This outcome not only directs future studies to consider the thermal patterns of less explored facial regions as possible correlates of mental states, but more importantly it suggests that different psychological states could potentially be discriminated in a contactless manner.

Different psychophysiological responses induced by a stimulus change during the orienting task and the Concealed Information Test

The orienting response (OR) account of the Concealed Information Test (CIT) posits that physiological responses to CIT items are components of the OR. Physiological variations within a stimulus sequence were investigated in an OR task (Study 1) and the CIT (Study 2). In Study 1, an unexpected increase in tone intensity was introduced after repeated standard tone presentations. The deviant tone elicited a large skin conductance response (SCR), heightened vascular tone, and self-reported surprise and also increased skin conductance level, self-reported arousal, and sustained vascular tone thereafter. In Study 2, the deviant relevant item presentation elicited a larger SCR and greater surprise compared with the frequent irrelevant item presentation, whereas vascular tone and self-reported arousal dropped after presentation of the relevant item. These results indicate that although phasic responses to a deviant stimulus were similar in both tasks, tonic variations following the stimulus change differed. Possible implications are discussed.

Sensitive Physiological Indices of Pain Based on Differential Characteristics of Electrodermal Activity

OBJECTIVE

Electrodermal activity (EDA) has been widely used to assess human response to stressful stimuli, including pain. Recently, spectral analysis of EDA has been found to be more sensitive and reproducible for assessment of sympathetic arousal than traditional indices (e.g., tonic and phasic components). However, none of the aforementioned analyses incorporate the differential characteristics of EDA, which could be more sensitive to capturing fast-changing dynamics associated with pain responses.

METHODS

We have tested the feasibility of using the derivative of phasic EDA and the modified time-varying spectral analysis of EDA. Sixteen subjects underwent four levels of pain stimulation using electric stimulation. Five-second segments of EDA were used for each level of stimulation, and pre-stimulation segments were considered stimulation level 0. We used support vector machines with the radial basis function kernel and multi-layer perceptron for three different scenarios of stimulation-level classification tasks: five stimulation levels (four levels of stimulation plus no stimulation); low, medium, and high pain stimulation (stimulation levels 0-1, 2, and 3-4, respectively); and high stimulation levels (stimulation levels 3-4) vs. no stimulation.

RESULTS

The maximum balanced accuracies were 44% (five stimulation levels), 63% (for low, medium, and high pain stimulation), and 87% (sensitivity 83% and specificity 89%, for high stimulation vs. no stimulation).

CONCLUSION

The differential characteristics of EDA contributed highly to the accuracy of pain stimulation level detection of the classifiers. The external validity dataset was not considered in the study.

SIGNIFICANCE

Our approach has the potential for accurate pain quantification using EDA.

Feasibility of combining heart rate variability and electrochemical skin conductance as screening and severity evaluation of cardiovascular autonomic neuropathy in type 2 diabetes

AIMS/INTRODUCTION

Clinical studies show that either heart rate variability (HRV) or electrochemical skin conductance (ESC) alone can serve as a simple and objective method for screening cardiovascular autonomic neuropathy (CAN). We tested the hypothesis that combining these two quantitative approaches can not only reinforce accuracy in CAN screening but also provide a better estimate of CAN severity in patients with type 2 diabetes (T2DM) who had already had CAN in outpatient clinics.

MATERIALS AND METHODS

Each patient received a complete battery of cardiovascular autonomic reflex tests (CARTs), with ESC measured by SUDOSCAN, time domain of HRV measured by standard deviation of all normal RR intervals (SDNN) and frequency domain of HRV (low frequency [LF], high frequency [HF], and LF/HF ratio), and peripheral blood studies for vascular risk factors. Severity of CAN was measured by CAN score.

RESULTS

The 90 T2DM patients included 50 males and 40 females. Those with more severe CAN had lower values in feet ESC (P = 0.023) and SDNN (P < 0.0001). Multiple linear regression analysis also showed that feet ESC and SDNN value (P = 0.003 and P < 0.0001) were significantly associated with CAN score. Combining SDNN and feet ESC also can increase the diagnostic accuracy of CAN with respective to sensitivity and specificity by using receiver operating characteristic analysis.

CONCLUSIONS

Combining the results of SDNN and feet ESC can not only assess, but also quantitatively reflect the progress or improvement of autonomic nerve function (including sympathetic and parasympathetic activity) in patients with T2DM.

A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity

Objective:

We present a statistical model for extracting physiologic characteristics from electrodermal activity (EDA) data in observational settings.

Methods:

We based our model on the integrate-and-fire physiology of sweat gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval structure. At the core of our model-based paradigm is a subject-specific amplitude threshold selection process for EDA pulses based on the statistical properties of four right-skewed models including the IG. By performing a sensitivity analysis across thresholds and fitting all four models, we selected for IG-like structure and verified the pulse selection with a goodness-of-fit analysis, maximizing capture of physiology at the time scale of EDA responses.

Results:

We tested the model-based paradigm on simulated EDA time series and data from two different experimental cohorts recorded during different experimental conditions, using different equipment. In both the simulated and experimental data, our model-based method robustly recovered pulses that captured the IG-like structure predicted by physiology, despite large differences in noise level. In contrast, established EDA analysis tools, which attempted to estimate neural activity from slower EDA responses, did not provide physiological validation and were susceptible to noise.

Conclusion:

We present a computationally efficient, statistically rigorous, and physiology-informed paradigm for pulse selection from EDA data that is robust across individuals and experimental conditions, yet adaptable to varying noise level.

Significance:

The robustness of the model-based paradigm and its physiological basis provide empirical support for the use of EDA as a clinical marker for sympathetic activity in conditions such as pain, anxiety, depression, and sleep states.

Electrodermal Activity of Auricular Acupoints in Pediatric Patients With Functional Abdominal Pain Disorders

OBJECTIVES

Functional abdominal pain disorders (FAPD) affect approximately 13.5% of children. Pharmacotherapy is often ineffective, leaving providers, and families seeking adjunctive therapies. Auriculotherapy provides treatment for pain and other symptoms, without a defined protocol for FAPD. A handheld point-finder device measuring transdermal electrical current determines active acupoints, with a higher current indicating a more active acupoint. Our objectives were to determine auricular acupoint (AA) activity in FAPD and to assess participants' attitudes towards auriculotherapy.

METHODS

This is a prospective double-blind study evaluating the electrodermal activity of AAs in pediatric-aged female participants with FAPD compared to healthy controls (HC). Participants completed surveys regarding demographics and interest in auriculotherapy. The electrodermal assessment evaluated 20 AAs per ear using a point-finder device. Each AA current measurement was analyzed by average relative rank and median, with a median current measurement ≥50 μA considered active.

RESULTS

We enrolled 46 female participants, 22 FAPD (mean age 15.8 years) and 24 HC (mean age 15.4 years). In FAPD, 12 of 40 AAs were active, of which only six were also active in HC. Comparison of median current and average ranking between participants demonstrated consistency. In the post-assessment survey, 86.4% of FAPD expressed interest in receiving auricular acupressure and 68.2% would travel to the clinic solely for treatment.

CONCLUSIONS

Based on electrodermal measurements, we propose a treatment protocol using auriculotherapy for FAPD symptom-management. We demonstrated there is considerable patient interest in auriculotherapy. Further studies are needed to confirm the findings in a larger sample size and validate the efficacy of this treatment protocol.

The auditory startle response in relation to outcome in functional movement disorders

BACKGROUND

The auditory startle reflex (ASR) is enlarged in patients with functional movement disorders (FMD).

OBJECTIVES

To study whether the ASR relates to symptom reduction in FMD patients, who participated in a placebo controlled double blind treatment trial with Botulinum Neurotoxin (BoNT).

METHODS

Response to treatment in the BoNT study was assessed using the Clinical Global Impression - Improvement scale (CGI-I). The electromyography (EMG) muscle activity of 7 muscles following 110 dB tones was measured in 14 FMD patients before and after one-year treatment and compared to 11 matched controls. The early and a late (behaviorally affected) component of the ASR and the sympathetic skin response (SSR) were assessed.

RESULTS

10 of 14 patients (71.4%) showed symptom improvement, which was believed to be mainly caused by placebo effects. The early total response probability of the ASR at baseline tended to be larger in patients compared to controls (p = 0.08), but normalized at follow-up (p = 0.84). The late total response probability was larger in patients vs. controls at baseline (p < 0.05), a trend that still was present at follow-up (p = 0.08). The SSR was higher in patients vs. controls at baseline (p < 0.01), and normalized at follow-up (p = 0.71).

CONCLUSIONS

On a group level 71.4% of the patients showed clinical symptom improvement after treatment. The early part of the ASR, most likely reflecting anxiety and hyperarousal, normalized in line with the clinical improvement. Interestingly, the augmented late component of the ASR remained enlarged suggesting persistent altered behavioral processing in functional patients despite motor improvement.

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

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 childrens 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 of discriminating clinically significant pain from clinically nonsignificant pain in children.

The Relative Influence of Drivers and Empaths on Team Synchronization

To further the understanding of how to build or reduce synchrony in a work team, we examined two principles for defining the optimal condition to produce or limit synchrony: (a) the empath-driver ratio (relative strength of the stronger influencer compared to the receptive strength of any member in the group), and (b) the balance between autocorrelated autonomic arousal (degree to which members' signals are independent of other group members) and the degree of influence that transfers from each group member to other group members. In study 1, we employed a series of computational simulations designed to manipulate the four variables. The results indicated that there is a four-way balance between driver strength, empath strength, autocorrelational and transfer effects among team members. The relationship between the synchronization coefficient and the empath-driver ratio was moderated by whether the group adopted a network structure for group problem solving or command-and-control. In study 2 we analyzed autonomic arousal (electrodermal response) in four teams of five participants playing a first-person shooter computer game. The correlation between the synchronization coefficient and the empath-driver ratio was 0.280 (p < .001) based on 64 pairs of observations. The relationship was moderated by both the network structure and the statistical model that one adopted to analyze dyadic relationships within the group. The implications of these relationships for a growing theory of team synchrony are discussed.

A dataset of daily ambulatory psychological and physiological recording for emotion research

To better understand the psychological and physiological basis of human emotion, increasing interest has been drawn towards ambulatory recordings of emotion-related data beyond the laboratories. By employing smartphones-based ambulatory assessment and wrist-worn physiological recording devices, the Daily Ambulatory Psychological and Physiological recording for Emotion Research (DAPPER) dataset provides momentary self-reports and physiological data of people's emotional experiences in their daily life. The dataset consists of ambulatory psychological recordings from 142 participants and physiological recordings from 88 of them over five days. Both the experience sampling method (ESM) and the day reconstruction method (DRM) were employed to have a comprehensive description of the participants' daily emotional experiences. Heart rate, galvanic skin response, and three-axis acceleration were recorded during the day time. By including multiple types of physiological and self-report data at a scale of five days with 100+ participants, the present dataset is expected to promote emotion researches in real-life, daily settings.

The effects of presenting additional stimuli resembling the CS+ during extinction on extinction retention and generalisation to novel stimuli

Recent studies have shown that extinction training including the conditional stimulus (CS+) and stimuli similar to the CS + enhances extinction retention and generalisation to novel stimuli. The aim of the present study was to confirm that these effects are specific to presenting stimuli similar to the CS+ during extinction and not merely an effect of additional trials or additional stimuli unrelated to the CS+. In an experiment conducted in a single session on the same day, participants (134 adults; 70.7% female; 17-40 years of age, M = 20.04, SD = 4.36) completed a habituation phase followed by an acquisition phase using dog images presented with (CS+) and without (CS-) a dog growl paired with a scream unconditional stimulus (US). Participants were randomly allocated to four extinction conditions: Multiple exemplar extinction comprising the CSs and two novel dog images similar to the CS+; Standard extinction control matched for the number of CS+ and CS- presentations; Extended extinction control matched for the total number extinction trials, and Other stimuli extinction control comprising the CSs and two novel stimuli unrelated to the CS+. All participants completed an extinction test with the original CSs followed by a generalisation test with two novel dog images. Multiple, Standard and Other stimuli extinction groups exhibited larger skin conductance responses (SCRs) during extinction to the CSs compared to the Extended extinction group. SCRs to the additional dog images in the Multiple group were larger than SCRs to the additional CSs in the Extended group and the novel images in the Other stimuli group. There were no group differences in responses to the CSs during extinction test. Unlike the other groups, SCRs to the first presentation of the novel generalisation dogs did not differ from those to the last CS trials in extinction in the Multiple group. However, this group difference did not persist beyond the initial generalisation trial. Finally, the Multiple, Extended, and Other stimuli groups exhibited more negative CS evaluations after extinction, extinction test, and generalisation test than the Standard extinction group. The results suggest that extinction with the original CSs and additional stimuli resembling the CS + elevated physiological responses during extinction and reduced physiological responses to novel stimuli similar to the CSs. Further studies are needed including clinical samples and trial-by-trial evaluations of the stimuli presented.

Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients' homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

Dynamic integration and segregation of amygdala subregional functional circuits linking to physiological arousal

The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

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.

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

Neurocognitive processing of infant stimuli in mothers and non-mothers: psychophysiological, cognitive and neuroimaging evidence

Emerging evidence indicates that mothers and non-mothers show different neurocognitive responses to infant stimuli. This study investigated mothers' psychophysiological, cognitive and neuronal responses to emotional infant stimuli. A total of 35 mothers with 4-month-old infants and 18 control women without young children underwent computerized tests assessing neurocognitive processing of infant stimuli. Their eye gazes and eye fixations, galvanic skin responses (GSRs) and facial expressions towards infant emotional stimuli were recorded during the tasks. Participants underwent functional magnetic resonance imaging during which they viewed pictures of an unknown infant and, for mothers, their own infants. Mothers gazed more and had increased GSR towards infant stimuli and displayed more positive facial expressions to infant laughter, and self-reported more positive ratings of infant vocalizations than control women. At a neural level, mothers showed greater neural response in insula, dorsolateral prefrontal cortex and occipital brain regions within a predefined 'maternal neural network' while watching images of their own vs unknown infants. This specific neural response to own infants correlated with less negative ratings of own vs unknown infants' signals of distress. Differences between mothers and control women without young children could be interpreted as neurocognitive adaptation to motherhood in the mothers.

Impaired fear learning and extinction, but not generalization, in anxious and non-anxious depression

Fear conditioning and generalization are well-known mechanisms in the pathogenesis of anxiety disorders. Extinction of conditioned fear responses is crucial for the psychotherapeutic treatment of these diseases. Anxious depression as a subtype of major depression shares characteristics with anxiety disorders. We therefore aimed to compare fear learning mechanisms in patients with anxious versus non-anxious depression. Fear learning mechanisms in patients with major depression (n = 79; for subgroup analyses n = 41 patients with anxious depression and n = 38 patients with non-anxious depression) were compared to 48 healthy participants. We used a well-established differential fear conditioning paradigm investigating acquisition, generalization, and extinction. Ratings of valence, arousal and probability of expected threat were assessed as well as skin conductance response as an objective psychophysiological measure. Patients with major depression showed impaired acquisition of conditioned fear. In addition, depressed patients showed impaired extinction of conditioned fear responses after successful fear conditioning. Generalization was not affected. However, there was no difference between patients with anxious and non-anxious depression. Results differed between objective and subjective measures. Our findings show altered fear acquisition and extinction in major depression as compared to healthy controls, but they do not favor differential fear learning and extinction mechanisms in the pathogenesis of anxious versus non-anxious depression. The results of impaired extinction warrant future studies addressing extinction learning elements in the treatment of depression.

A Bio-Physio-Psychological Investigation of Athletes' Burnout

Purpose: Changes in biophysiological markers related to perceived burnout and self-comfort were tested in this study. Method: Forty-two student-athletes from middle and high school grades were evaluated for burnout, salivary cortisol levels, measures of arousal-related physiological markers (i.e., blood volume pulse; BVP), galvanic skin response (GSR), and respiratory rate, and self-comfort variables during the last two weeks of the season. Using self-comfort theory as its conceptual framework, we examined burnout through a conceptual model in which feeling of discomfort with the self was related to biophysiological markers affecting perceived burnout. The proposed model was tested by using a partial least squares structural equation modeling (PLS-SEM). Results: The main findings indicate that increased self-discomfort is significantly (p < .001) associated with increased salivary cortisol (β = - 0.189) along with a significant (p = .050) decrease in GSR (β = - 0.259). Increased salivary cortisol is significantly (p < .001) associated with increased burnout (β = 0.242). Conclusion: The findings partially support the model and encourage further effort to capture the burnout syndrome through the integration of biological and psychological markers.

Parasympathetic-Sympathetic Causal Interactions Assessed by Time-Varying Multivariate Autoregressive Modeling of Electrodermal Activity and Heart-Rate-Variability

OBJECTIVE

Most of the bodily functions are regulated by multiple interactions between the parasympathetic (PNS) and sympathetic (SNS) nervous system. In this study, we propose a novel framework to quantify the causal flow of information between PNS and SNS through the analysis of heart rate variability (HRV) and electrodermal activity (EDA) signals.

METHODS

Our method is based on a time-varying (TV) multivariate autoregressive model of EDA and HRV time-series and incorporates physiologically inspired assumptions by estimating the Directed Coherence in a specific frequency range. The statistical significance of the observed interactions is assessed by a bootstrap procedure purposely developed to infer causalities in the presence of both TV model coefficients and TV model residuals (i.e., heteroskedasticity). We tested our method on two different experiments designed to trigger a sympathetic response, i.e., a hand-grip task (HG) and a mental-computation task (MC).

RESULTS

Our results show a parasympathetic driven interaction in the resting state, which is consistent across different studies. The onset of the stressful stimulation triggers a cascade of events characterized by the presence or absence of the PNS-SNS interaction and changes in the directionality. Despite similarities between the results related to the two tasks, we reveal differences in the dynamics of the PNS-SNS interaction, which might reflect different regulatory mechanisms associated with different stressors.

CONCLUSION

We estimate causal coupling between PNS and SNS through MVAR modeling of EDA and HRV time-series.

SIGNIFICANCE

Our results suggest promising future applicability to investigate more complex contexts such as affective and pathological scenarios.

Sleep to remember, sleep to forget: Rapid eye movement sleep can have inverse effects on recall and generalization of fear memories

Rapid Eye Movement (REM) sleep has been shown to modulate the consolidation of fear memories, a process that may contribute to the development of Post-Traumatic Stress Disorder (PTSD). However, contradictory findings have been reported regarding the direction of this modulation and its differential effects on recall versus generalization. In two complementary experiments, we addressed this by employing sleep deprivation protocols together with a novel fear-conditioning paradigm that required the discrimination between coexisting threat and safety signals. Using skin conductance responses and functional imaging (fMRI), we found two opposing effects of REM sleep: While REM impaired recall of the original threat memories, it improved the ability to generalize these memories to novel situations that emphasized the discrimination between threat and safety signals. These results, as well as previous findings in healthy participants and patients diagnosed with PTSD, could be explained by the degree to which the balance between threat and safety signals for a given stimulus was predictive of threat. We suggest that this account can be integrated with contemporary theories of sleep and fear learning, such as the REM recalibration hypothesis.

Infants relax in response to unfamiliar foreign lullabies

Music is characterized by acoustic forms that are predictive of its behavioural functions. For example, adult listeners accurately identify unfamiliar lullabies as infant-directed on the basis of their musical features alone. This property could reflect a function of listeners' experiences, the basic design of the human mind, or both. Here, we show that US infants (N = 144) relax in response to eight unfamiliar foreign lullabies, relative to matched non-lullaby songs from other foreign societies, as indexed by heart rate, pupillometry and electrodermal activity. They do so consistently throughout the first year of life, suggesting that the response is not a function of their musical experiences, which are limited relative to those of adults. The infants' parents overwhelmingly chose lullabies as the songs that they would use to calm their fussy infant, despite their unfamiliarity. Together, these findings suggest that infants may be predisposed to respond to common features of lullabies found in different cultures.

Wearable Sensors for Assessing the Role of Olfactory Training on the Autonomic Response to Olfactory Stimulation

Wearable sensors are nowadays largely employed to assess physiological signals derived from the human body without representing a burden in terms of obtrusiveness. One of the most intriguing fields of application for such systems include the assessment of physiological responses to sensory stimuli. In this specific regard, it is not yet known which are the main psychophysiological drivers of olfactory-related pleasantness, as the current literature has demonstrated the relationship between odor familiarity and odor valence, but has not clarified the consequentiality between the two domains. Here, we enrolled a group of university students to whom olfactory training lasting 3 months was administered. Thanks to the analysis of electrocardiogram (ECG) and galvanic skin response (GSR) signals at the beginning and at the end of the training period, we observed different autonomic responses, with higher parasympathetically-mediated response at the end of the period with respect to the first evaluation. This possibly suggests that an increased familiarity to the proposed stimuli would lead to a higher tendency towards relaxation. Such results could suggest potential applications to other domains, including personalized treatments based on odors and foods in neuropsychiatric and eating disorders.

Physiological responses to proposals during dyadic decision-making conversations

A novel conversation-analytically informed paradigm was used to examine how joint decision-making interaction, with its various types of proposal sequences, is reflected in the physiological responses of participants. Two types of dyads–dyads with one depressed and one non-depressed participant (N = 15) and dyads with two non-depressed participants (N = 15)–engaged in a series of conversational joint decision-making tasks, during which we measured their skin conductance (SC) responses. We found that the participants’ SC response rates were higher and more synchronized during proposal sequences than elsewhere in the conversation. Furthermore, SC response rates were higher when the participant was in the role of a proposal speaker (vs. a proposal recipient), and making a proposal was associated with higher SC response rates for participants with depression (vs. participants without depression). Moreover, the SC response rates in the proposal speaker were higher when the recipient accepted (vs. not accepted) the proposal. We interpret this finding with reference to accepting responses suggesting a commitment to future action, for which the proposal speaker may feel specifically responsible for. A better understanding of the physiological underpinnings of joint decision-making interaction may help improve democratic practices in contexts where certain individuals experience challenges in this regard.

Soft Wireless Bioelectronics and Differential Electrodermal Activity for Home Sleep Monitoring

Sleep is an essential element to human life, restoring the brain and body from accumulated fatigue from daily activities. Quantitative monitoring of daily sleep quality can provide critical feedback to evaluate human health and life patterns. However, the existing sleep assessment system using polysomnography is not available for a home sleep evaluation, while it requires multiple sensors, tabletop electronics, and sleep specialists. More importantly, the mandatory sleep in a designated lab facility disrupts a subject’s regular sleep pattern, which does not capture one’s everyday sleep behaviors. Recent studies report that galvanic skin response (GSR) measured on the skin can be one indicator to evaluate the sleep quality daily at home. However, the available GSR detection devices require rigid sensors wrapped on fingers along with separate electronic components for data acquisition, which can interrupt the normal sleep conditions. Here, we report a new class of materials, sensors, electronics, and packaging technologies to develop a wireless, soft electronic system that can measure GSR on the wrist. The single device platform that avoids wires, rigid sensors, and straps offers the maximum comfort to wear on the skin and minimize disruption of a subject’s sleep. A nanomaterial GSR sensor, printed on a soft elastomeric membrane, can have intimate contact with the skin to reduce motion artifact during sleep. A multi-layered flexible circuit mounted on top of the sensor provides a wireless, continuous, real-time recording of GSR to classify sleep stages, validated by the direct comparison with the standard method that measures other physiological signals. Collectively, the soft bioelectronic system shows great potential to be working as a portable, at-home sensor system for assessing sleep quality before a hospital visit.

Peripheral Nerve Conduction And Sympathetic Skin Response Are Reliable Methods to Detect Diabetic Cardiac Autonomic Neuropathy

AIM

This study aimed to investigate the role of nerve conduction studies (NCS) and sympathetic skin response (SSR) in evaluating diabetic cardiac autonomic neuropathy (DCAN).

METHODS

DCAN was diagnosed using the Ewing test combined with heart rate variability analysis. NCS and SSR were assessed by electrophysiological methods. The association between NCS/SSR and DCAN was assessed via multivariate regression and receiver-operating characteristic analyses.

RESULTS

The amplitude and conduction velocity of the motor/sensory nerve were found to be significantly lower in the DCAN+ group (all P < 0.05). A lower amplitude of peroneal nerve motor fiber was found to be associated with increased odds for DCAN (OR 2.77, P < 0.05). The SSR amplitude was lower while the SSR latency was longer in the DCAN+ group than in the DCAN- group. The receiver-operating characteristic analysis revealed that the optimal cutoff points of upper/lower limb amplitude of SSR to indicate DCAN were 1.40 mV (sensitivity, 61.9%; specificity, 66.3%, P < 0.001) and 0.85 mV (sensitivity, 66.7%; specificity, 68.5%, P < 0.001), respectively. The optimal cutoff points of upper/lower limb latency to indicate DCAN were 1.40 s (sensitivity, 61.9%; specificity, 62%, P < 0.05) and 1.81 s (sensitivity, 69.0%; specificity, 52.2%, P < 0.05), respectively.

CONCLUSIONS

NCS and SSR are reliable methods to detect DCAN. Abnormality in the peroneal nerve (motor nerve) is crucial in predicting DCAN. SSR may help predict DCAN.

A Comparison of Four Dyadic Synchronization Models

Synchronization is a special case of self-organization in which one can observe close mimicry in behavior of the system components. Synchrony in body movements, autonomic arousal, and EEG activity among human individuals has attracted considerable attention for their possible roles in social interaction. This article is specifically concerned with autonomic synchrony and finding the best model for the dyadic relationships, with regard to both theoretical and empirical accuracy, that could be extrapolated to synchrony levels for groups and teams of three or more people. The four models that are compared in this study have different theoretical origins: the two-variable linear regression function, a three-parameter nonlinear regression function, the logistic map function stated in polynomial form, and the logistic map function stated as an exponential regression structure. The data for this study were electrodermal responses collected from a team of four people engaged in an emergency response simulation that produced 12 dyadic time series. Results shows strong levels of fit between the data and all four models, although there were significant differences among them. Further research directions point toward finding conditions that favor one model over another and exploring other possible nonlinear structures.

Emotion matters: Different psychophysiological responses to expressive and non-expressive full-body movements

We explore dance video clip stimuli as a means to test human observers' accuracy in detecting genuine emotional expressivity in full-body movements. Stimuli of every-day-type full-body expressions of emotions usually use culturally very recognizable actions (e.g. fist shaking for anger, etc). However, expressive dance movement stimuli can be created to contain fully abstract movements. The expressivity results from subtle variations in the body movements of the expressor, and emotions cannot be recognised by observers via particular actions (e.g. fist shaking, etc). Forty-one participants watched and rated 24 pairs of short dance videos -from a published normalised dance stimuli library- in randomised order (N = 48). Of each carefully matched pair, one version of the full-body movement sequence had been danced to be emotionally genuinely expressive (clip a), while the other version of the same sequence (clip b) had been danced -while technically correct- without any emotional expressivity. Participants rated (i) expressivity (to test their accuracy; block 1), and (ii) how much they liked each movement (an implicit measure to test their emotional response ("liking"); block 2). Participants rated clips that were intended to be expressive as more expressive (part 1: expressivity ratings), and liked those expressive clips more than the non-expressive clips (part 2: liking ratings). Besides, their galvanic skin response differed, depending on the category of clips they were watching (expressive vs. non-expressive), and this relationship was modulated by interceptive accuracy and arts experience. Results are discussed in relation to the Body Precision Hypothesis and the Hypothesis of Constructed Emotion.

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.

Predicting driver takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers' physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers' takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers' takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.

Behavioral, Physiological and EEG Activities Associated with Conditioned Fear as Sensors for Fear and Anxiety

Anxiety disorders impose substantial costs upon public health and productivity in the USA and worldwide. At present, these conditions are quantified by self-report questionnaires that only apply to behaviors that are accessible to consciousness, or by the timing of responses to fear- and anxiety-related words that are indirect since they do not produce fear, e.g., Dot Probe Test and emotional Stroop. We now review the conditioned responses (CRs) to fear produced by a neutral stimulus (conditioned stimulus CS+) when it cues a painful laser unconditioned stimulus (US). These CRs include autonomic (Skin Conductance Response) and ratings of the CS+ unpleasantness, ability to command attention, and the recognition of the association of CS+ with US (expectancy). These CRs are directly related to fear, and some measure behaviors that are minimally accessible to consciousness e.g., economic scales. Fear-related CRs include non-phase-locked phase changes in oscillatory EEG power defined by frequency and time post-stimulus over baseline, and changes in phase-locked visual and laser evoked responses both of which include late potentials reflecting attention or expectancy, like the P300, or contingent negative variation. Increases (ERS) and decreases (ERD) in oscillatory power post-stimulus may be generalizable given their consistency across healthy subjects. ERS and ERD are related to the ratings above as well as to anxious personalities and clinical anxiety and can resolve activity over short time intervals like those for some moods and emotions. These results could be incorporated into an objective instrumented test that measures EEG and CRs of autonomic activity and psychological ratings related to conditioned fear, some of which are subliminal. As in the case of instrumented tests of vigilance, these results could be useful for the direct, objective measurement of multiple aspects of the risk, diagnosis, and monitoring of therapies for anxiety disorders and anxious personalities.

Analysis of Consciousness Level Using Galvanic Skin Response during Therapeutic Effect

The neurological status of patients in the Intensive Care Units (ICU) is determined by the Glasgow Coma Scale (GCS). Patients in coma are thought to be unaware of what is happening around them. However, many studies show that the family plays an important role in the recovery of the patient and is a great emotional resource. In this study, Galvanic Skin Response (GSR) signals were analyzed from 31 patients with low consciousness levels between GCS 3 and 8 to determine relationship between consciousness level and GSR signals as a new approach. The effect of family and nurse on unconscious patients was investigated by GSR signals recorded with a new proposed protocol. The signals were recorded during conversation and touching of the patient by the nurse and their families. According to numerical results, the level of consciousness can be separated using GSR signals. Also, it was found that family and nurse had statistically significant effects on the patient. Patients with GCS 3,4, and 5 were considered to have low level of consciousness, while patients with GCS 6,7, and 8 were considered to have high level of consciousness. According to our results, it is obtained lower GSR amplitude in low GCS (3, 4, 5) compared to high GCS (7, 8). It was concluded that these patients were aware of therapeutic affect although they were unconscious. During the classification stage of this study, the class imbalance problem, which is common in medical diagnosis, was solved using Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random oversampling methods. In addition, level of consciousness was classified with 92.7% success using various decision tree algorithms. Random Forest was the method which provides higher accuracy compared to all other methods. The obtained results showed that GSR signal analysis recorded in different stages gives very successful GCS score classification performance according to literature studies.

Car Driver's Sympathetic Reaction Detection Through Electrodermal Activity and Electrocardiogram Measurements

OBJECTIVE

in this paper we propose a system to detect a subject's sympathetic reaction, which is related to unexpected or challenging events during a car drive.

METHODS

we use the Electrocardiogram (ECG) signal and the Skin Potential Response (SPR) signal, which has several advantages with respect to other Electrodermal (EDA) signals. We record one SPR signal for each hand, and use an algorithm that, selecting the smoother signal, is able to remove motion artifacts. We extract statistical features from the ECG and SPR signals in order to classify signal segments and identify the presence or absence of emotional events via a Supervised Learning Algorithm. The experiments were carried out in a company which specializes in driving simulator equipment, using a motorized platform and a driving simulator. Different subjects were tested with this setup, with different challenging events happening on predetermined locations on the track.

RESULTS

we obtain an Accuracy as high as 79.10% for signal blocks and as high as 91.27% for events.

CONCLUSION

results demonstrate the good performance of the presented system in detecting sympathetic reactions, and the effectiveness of the motion artifact removal procedure.

SIGNIFICANCE

our work demonstrates the possibility to classify the emotional state of the driver, using the ECG and EDA signals and a slightly invasive setup. In particular, the proposed use of SPR and of the motion artifact removal procedure are crucial for the effectiveness of the system.

Sensation seeking and neuroticism in fear conditioning and extinction: The role of avoidance behaviour

Maladaptive avoidance behaviour, a key symptom of anxiety-related disorders, prevents extinction learning and maintains anxiety. Individual personality traits likely influence avoidance propensity: high sensation-seeking may decrease avoidance, thereby increasing extinction, and neuroticism may have the reverse effect. However, research on this is scarce. Using a naturalistic conditioned avoidance paradigm, 163 women underwent differential fear acquisition to a conditioned stimulus (CSplus). Next, during extinction, participants could either choose a risky shortcut, anticipating shock signalled by CSplus, or a time-consuming avoidance option (lengthy detour). Across participants, increased skin conductance (SCR) acquisition learning predicted subsequent instrumental avoidance. Avoidance, in turn, predicted elevated post-extinction SCR and shock-expectancy, i.e., 'protection-from-extinction'. Mediation analyses revealed that sensation seeking decreased protection-from-extinction-both for shock-expectancy and SCR-via attenuating avoidance. Neither sensation seeking nor neuroticism were related to acquisition learning and neuroticism was neither related to avoidance nor extinction. Transcranial direct currentstimulation administered before extinction did not influence present results. Results highlight the important role of elevated avoidance propensity in fear maintenance. Results moreover provide evidence for reduced sensation-seeking and increased acquisition learning to be avoidance-driving mechanisms. Since approach-avoidance conflicts are faced by anxiety patients on a daily basis, strengthening sensation-seeking-congruent attitudes and approach behaviours may optimize individualized treatment.

Physiological synchrony is associated with cooperative success in real-life interactions

Cooperation is pivotal for society to flourish. To foster cooperation, humans express and read intentions via explicit signals and subtle reflections of arousal visible in the face. Evidence is accumulating that humans synchronize these nonverbal expressions and the physiological mechanisms underlying them, potentially influencing cooperation. The current study is designed to verify this putative linkage between synchrony and cooperation. To that end, 152 participants played the Prisoner's Dilemma game in a dyadic interaction setting, sometimes facing each other and sometimes not. Results showed that synchrony in both heart rate and skin conductance level emerged during face-to-face contact. However, only synchrony in skin conductance levels predicted cooperative success of dyads. Crucially, this positive linkage was strengthened when participants could see each other. These findings show the strong relationship between our bodily responses and social behavior, and emphasize the importance of studying social processes between rather than within individuals in real-life interactions.

Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors

OBJECTIVE

Sweat secretions lead to variations in skin conductance (SC) signal. The relatively fast variation of SC, called the phasic component, reflects sympathetic nervous system activity. The slow variation related to thermoregulation and general arousal is known as the tonic component. It is challenging to decompose the SC signal into its constituents to decipher the encoded neural information related to emotional arousal.

METHODS

We model the phasic component using a second-order differential equation representing the diffusion and evaporation processes of sweating. We include a sparse impulsive neural signal that stimulates the sweat glands for sweat production. We model the tonic component with several cubic B-spline functions. We formulate an optimization problem with physiological priors on system parameters, a sparsity prior on the neural stimuli, and a smoothness prior on the tonic component. Finally, we employ a generalized-cross-validation-based coordinate descent approach to balance among the smoothness of the tonic component, the sparsity of the neural stimuli, and the residual.

RESULTS

We illustrate that we can successfully recover the unknowns separating both tonic and phasic components from both experimental and simulated data (with ). Further, we successfully demonstrate our ability to automatically identify the sparsity level for the neural stimuli and the smoothness level for the tonic component.

CONCLUSION

Our generalized-cross-validation-based novel method for SC signal decomposition successfully addresses previous challenges and retrieves a physiologically plausible solution.

SIGNIFICANCE

Accurate decomposition of SC could potentially improve cognitive stress tracking in patients with mental disorders.